col.width <- c(37, 10, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 4, 4, 1, 4, 1, 1,
               1, 3, 2, 2, 8, 2, 5, 5, 5, 4, 5, 5, 5,4, 2, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3,
               3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 8,
               8, 8, 2, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 2, 2, 5, 2, 5, 3, 1, 3, 1, 8, 8, 2, 8,
               2, 8, 2, 2, 1, 8, 1, 1, 1, 1, 1, 8, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 3, 1, 1,
               1, 1, 1, 1, 1, 1, 1)
col.names.abr <- c("PUF_CASE_ID", "PUF_FACILITY_ID", "FACILITY_TYPE_CD", "FACILITY_LOCATION_CD",
                   "AGE", "SEX", "RACE", "SPANISH_HISPANIC_ORIGIN", "INSURANCE_STATUS",
                   "MED_INC_QUAR_00", "NO_HSD_QUAR_00", "UR_CD_03", "MED_INC_QUAR_12", "NO_HSD_QUAR_12",
                   "UR_CD_13", "CROWFLY", "CDCC_TOTAL_BEST", "SEQUENCE_NUMBER", "CLASS_OF_CASE",
                   "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "LATERALITY", "HISTOLOGY", "BEHAVIOR", "GRADE",
                   "DIAGNOSTIC_CONFIRMATION", "TUMOR_SIZE", "REGIONAL_NODES_POSITIVE",
                   "REGIONAL_NODES_EXAMINED", "DX_STAGING_PROC_DAYS", "RX_SUMM_DXSTG_PROC", "TNM_CLIN_T",
                   "TNM_CLIN_N", "TNM_CLIN_M", "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                   "TNM_PATH_STAGE_GROUP", "TNM_EDITION_NUMBER", "ANALYTIC_STAGE_GROUP", "CS_METS_AT_DX",
                   "CS_METS_EVAL", "CS_EXTENSION", "CS_TUMOR_SIZEEXT_EVAL", "CS_METS_DX_BONE", "CS_METS_DX_BRAIN",
                   "CS_METS_DX_LIVER", "CS_METS_DX_LUNG", "LYMPH_VASCULAR_INVASION", "CS_SITESPECIFIC_FACTOR_1",
                   "CS_SITESPECIFIC_FACTOR_2", "CS_SITESPECIFIC_FACTOR_3", "CS_SITESPECIFIC_FACTOR_4",
                   "CS_SITESPECIFIC_FACTOR_5", "CS_SITESPECIFIC_FACTOR_6", "CS_SITESPECIFIC_FACTOR_7",
                   "CS_SITESPECIFIC_FACTOR_8", "CS_SITESPECIFIC_FACTOR_9", "CS_SITESPECIFIC_FACTOR_10",
                   "CS_SITESPECIFIC_FACTOR_11", "CS_SITESPECIFIC_FACTOR_12", "CS_SITESPECIFIC_FACTOR_13",
                   "CS_SITESPECIFIC_FACTOR_14", "CS_SITESPECIFIC_FACTOR_15", "CS_SITESPECIFIC_FACTOR_16",
                   "CS_SITESPECIFIC_FACTOR_17", "CS_SITESPECIFIC_FACTOR_18", "CS_SITESPECIFIC_FACTOR_19",
                   "CS_SITESPECIFIC_FACTOR_20", "CS_SITESPECIFIC_FACTOR_21", "CS_SITESPECIFIC_FACTOR_22",
                   "CS_SITESPECIFIC_FACTOR_23", "CS_SITESPECIFIC_FACTOR_24", "CS_SITESPECIFIC_FACTOR_25",
                   "CS_VERSION_LATEST", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS", "DX_DEFSURG_STARTED_DAYS",
                   "RX_SUMM_SURG_PRIM_SITE", "RX_HOSP_SURG_APPR_2010", "RX_SUMM_SURGICAL_MARGINS",
                   "RX_SUMM_SCOPE_REG_LN_SUR", "RX_SUMM_SURG_OTH_REGDIS", "SURG_DISCHARGE_DAYS", "READM_HOSP_30_DAYS",
                   "REASON_FOR_NO_SURGERY", "DX_RAD_STARTED_DAYS", "RX_SUMM_RADIATION", "RAD_LOCATION_OF_RX",
                   "RAD_TREAT_VOL", "RAD_REGIONAL_RX_MODALITY", "RAD_REGIONAL_DOSE_CGY", "RAD_BOOST_RX_MODALITY",
                   "RAD_BOOST_DOSE_CGY", "RAD_NUM_TREAT_VOL", "RX_SUMM_SURGRAD_SEQ", "RAD_ELAPSED_RX_DAYS",
                   "REASON_FOR_NO_RADIATION", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", "RX_SUMM_CHEMO",
                   "DX_HORMONE_STARTED_DAYS", "RX_SUMM_HORMONE", "DX_IMMUNO_STARTED_DAYS", "RX_SUMM_IMMUNOTHERAPY",
                   "RX_SUMM_TRNSPLNT_ENDO", "RX_SUMM_SYSTEMIC_SUR_SEQ", "DX_OTHER_STARTED_DAYS", "RX_SUMM_OTHER",
                   "PALLIATIVE_CARE", "RX_SUMM_TREATMENT_STATUS", "PUF_30_DAY_MORT_CD", "PUF_90_DAY_MORT_CD",
                   "DX_LASTCONTACT_DEATH_MONTHS", "PUF_VITAL_STATUS", "RX_HOSP_SURG_PRIM_SITE", "RX_HOSP_CHEMO",
                   "RX_HOSP_IMMUNOTHERAPY", "RX_HOSP_HORMONE", "RX_HOSP_OTHER", "PUF_MULT_SOURCE", "REFERENCE_DATE_FLAG",
                   "RX_SUMM_SCOPE_REG_LN_2012", "RX_HOSP_DXSTG_PROC", "PALLIATIVE_CARE_HOSP", "TUMOR_SIZE_SUMMARY",
                   "METS_AT_DX_OTHER", "METS_AT_DX_DISTANT_LN", "METS_AT_DX_BONE", "METS_AT_DX_BRAIN",
                   "METS_AT_DX_LIVER", "METS_AT_DX_LUNG", "NO_HSD_QUAR_16", "MED_INC_QUAR_16", "MEDICAID_EXPN_CODE")
#Read in data for each subsite
lip <- read_fwf('NCDBPUF_Lip.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
melanoma <- read_fwf('NCDBPUF_Melanoma.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
                       
skin <- read_fwf('NCDBPUF_OtSkin.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
hodgextr <- read_fwf('NCDBPUF_HodgExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
hodgndal <- read_fwf('NCDBPUF_HodgNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
NHLndal <- read_fwf('NCDBPUF_NHLNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
#Combine data for all subsites
dat <- bind_rows(lip, melanoma, skin, hodgextr, hodgndal, NHLndal)
rm(lip, melanoma, skin, hodgextr, hodgndal, NHLndal)
#only works for melanoma
prim_site_text <- data_frame(PRIMARY_SITE = c("C440",
"C441",
"C442",
"C443",
"C444",
"C445",
"C446",
"C447",
"C448",
"C449", 
"C510",
"C511",
"C512",
"C518",
"C519",
"C600",
"C601",
"C602",
"C608",
"C609",
"C632"),                
SITE_TEXT = c(
"C44.0 Skin of lip, NOS",
"C44.1 Eyelid",
"C44.2 External ear",
"C44.3 Skin of ear and unspecified parts of face",
"C44.4 Skin of scalp and neck",
"C44.5 Skin of trunk",
"C44.6 Skin of upper limb and shoulder",
"C44.7 Skin of lower limb and hip",
"C44.8 Overlapping lesion of skin",
"C44.9 Skin, NOS", 
"C51.0 Labium majus",
"C51.1 Labium minus",
"C51.2 Clitoris",
"C51.8 Overlapping lesion of vulva",
"C51.9 Vulva, NOS",
"C60.0 Prepuce",
"C60.1 Glans penis",
"C60.2 Body of penis",
"C60.8 Overlapping lesion of penis",
"C60.9 Penis",
"C63.2 Scrotum, NOS"))
dat <- merge(dat, prim_site_text, by = "PRIMARY_SITE", all.x = TRUE) 
 
rm(prim_site_text)
# convert numeric variables from character class to numeric class
num_vars <- c("AGE", "CROWFLY", "TUMOR_SIZE", "DX_STAGING_PROC_DAYS", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
              "DX_DEFSURG_STARTED_DAYS", "SURG_DISCHARGE_DAYS", "DX_RAD_STARTED_DAYS",  "RAD_REGIONAL_DOSE_CGY",
              "RAD_BOOST_DOSE_CGY", "RAD_ELAPSED_RX_DAYS", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", 
              "DX_HORMONE_STARTED_DAYS", "DX_OTHER_STARTED_DAYS", "DX_LASTCONTACT_DEATH_MONTHS",
              "RAD_NUM_TREAT_VOL")
dat[num_vars] <- lapply(dat[num_vars], as.numeric)
# convert factor variables from character class to factor class
vars <- names(dat)
fact_vars <- vars[!(vars %in% num_vars)] # basically all of the non-numerics
dat[fact_vars] <- lapply(dat[fact_vars], as.character)
dat[fact_vars] <- lapply(dat[fact_vars], as.factor)
dat <- dat %>%
        mutate(FACILITY_TYPE_F = fct_recode(FACILITY_TYPE_CD,
                                            "Community Cancer Program" = "1",
                                            "Comprehensive Comm Ca Program" = "2",
                                            "Academic/Research Program" = "3",
                                            "Integrated Network Ca Program" = "4",
                                            "Other" = "9")) %>%
        mutate(FACILITY_LOCATION_F = fct_recode(FACILITY_LOCATION_CD,
                                            "New England" = "1",
                                            "Middle Atlantic" = "2",
                                            "South Atlantic" = "3",
                                            "East North Central" = "4",
                                            "East South Central" = "5",
                                            "West North Central" = "6",
                                            "West South Central" = "7",
                                            "Mountain" = "8",
                                            "Pacific" = "9",
                                            "out of US" = "0")) %>%
        mutate(FACILITY_GEOGRAPHY = fct_collapse(FACILITY_LOCATION_CD,
                                                 "Northeast" = c("1", "2"),
                                                 "South" = c("3", "7"),
                                                 "Midwest" = c("4", "5", "6"),
                                                 "West" = c("8", "9"))) %>%
        mutate(AGE_F = cut(AGE, c(0, 54, 64, 74, 100))) %>%
        mutate(AGE_40 = cut(AGE, c(0, 40, 100))) %>%
        mutate(SEX_F = fct_recode(SEX,
                                "Male" = "1",
                                "Female" = "2")) %>%
        mutate(RACE_F = fct_collapse(RACE,
                                "White" = c("01"),
                                "Black" = c("02"),
                                "Asian" = c("04", "05", "06", "07", "08", "10", "11", "12", "13", "14", "15",
                                            "16", "17", "20", "21", "22", "25", "26", "27", "28", "30", "31",
                                            "32", "96", "97"),
                                "Other/Unk" = c("03", "98", "99"))) %>%
        mutate(HISPANIC = fct_collapse(SPANISH_HISPANIC_ORIGIN,
                                       "Yes" = c("1", "2", "3", "4", "5", "6", "7", "8"),
                                       "No" = c("0"),
                                       "Unknown" = c("9"))) %>%
        mutate(INSURANCE_F = fct_recode(INSURANCE_STATUS,
                                         "None" = "0",
                                         "Private" = "1",
                                         "Medicaid" = "2",
                                         "Medicare" = "3",
                                         "Other Government" = "4",
                                         "Unknown" = "9")) %>%
        mutate(INSURANCE_F = fct_relevel(INSURANCE_F,
                                         "Private")) %>%
        mutate(INCOME_F = fct_recode(MED_INC_QUAR_12,
                                      "Less than $38,000" = "1",
                                      "$38,000 - $47,999" = "2",
                                      "$48,000 - $62,999" = "3",
                                      "$63,000 +" = "4")) %>%
        mutate(EDUCATION_F = fct_recode(NO_HSD_QUAR_12,
                                        "21% or more" = "1",
                                        "13 - 20.9%" = "2",
                                        "7 - 12.9%" = "3",
                                        "Less than 7%" = "4")) %>%
        mutate(U_R_F = fct_collapse(UR_CD_13,
                                    "Metro" = c("1", "2", "3"),
                                    "Urban" = c("4", "5", "6", "7"),
                                    "Rural" = c("8", "9"))) %>%
        mutate(CLASS_OF_CASE_F = fct_collapse(CLASS_OF_CASE,
                                              All_Part_Prim = c("10", "11", "12", "13",
                                                                "14", "20", "21", "22"),
                                              Other_Facility = c("00"))) %>%
        mutate(GRADE_F = fct_recode(GRADE,
                                  "Gr I: Well Diff" = "1",
                                  "Gr II: Mod Diff" = "2",
                                  "Gr III: Poor Diff" = "3",
                                  "Gr IV: Undiff/Anaplastic" = "4",
                                  "NA/Unkown" = "9")) %>%
        mutate(HISTOLOGY_F = fct_infreq(HISTOLOGY)) %>%
        mutate(HISTOLOGY_F = factor(HISTOLOGY_F)) %>%
        mutate(HISTOLOGY_F_LIM = fct_lump(HISTOLOGY_F, prop = 0.05)) %>%
        mutate(TNM_CLIN_T = fct_recode(TNM_CLIN_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_T = fct_relevel(TNM_CLIN_T,
                                        "1")) %>%
        mutate(TNM_CLIN_N = fct_recode(TNM_CLIN_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_M = fct_recode(TNM_CLIN_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_recode(TNM_PATH_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_relevel(TNM_PATH_T,
                                        "1")) %>%
        mutate(TNM_PATH_N = fct_recode(TNM_PATH_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_M = fct_recode(TNM_PATH_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_STAGE_GROUP = fct_recode(TNM_CLIN_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_STAGE_GROUP = fct_recode(TNM_PATH_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(MARGINS = fct_recode(RX_SUMM_SURGICAL_MARGINS,
                                    "No Residual" = "0",
                                    "Residual, NOS" = "1",
                                    "Microscopic Resid" = "2",
                                    "Macroscopic Resid" = "3",
                                    "Not evaluable" = "7",
                                    "No surg" = "8",
                                    "Unknown" = "9")) %>%
        mutate(MARGINS_YN = fct_collapse(RX_SUMM_SURGICAL_MARGINS,
                                         "Yes" = c("1", "2", "3"),
                                         "No" = c("0"),
                                         "No surg/Unk/NA" = c("7", "8", "9"))) %>%
        mutate(READM_HOSP_30_DAYS_F = fct_recode(READM_HOSP_30_DAYS,
                                                 "No_Surg_or_No_Readmit" = "0",
                                                 "Unplan_Readmit_Same" = "1",
                                                 "Plan_Readmit_Same" = "2",
                                                 "PlanUnplan_Same" = "3",
                                                 "Unknown" = "4")) %>%
        mutate(RX_SUMM_RADIATION_F = fct_recode(RX_SUMM_RADIATION,
                                                "None" = "0",
                                                "Beam Radiation" = "1",
                                                "Radioactive Implants" = "2",
                                                "Radioisotopes" = "3",
                                                "Beam + Imp or Isotopes" = "4",
                                                "Radiation, NOS" = "5",
                                                "Unknown" = "9")) %>%
        mutate(PUF_30_DAY_MORT_CD_F = fct_recode(PUF_30_DAY_MORT_CD,
                                                 "Alive_30" = "0",
                                                 "Dead_30" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(PUF_90_DAY_MORT_CD_F = fct_recode(PUF_90_DAY_MORT_CD,
                                                 "Alive_90" = "0",
                                                 "Dead_90" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(LYMPH_VASCULAR_INVASION_F = fct_recode(LYMPH_VASCULAR_INVASION,
                                                      "Neg_LymphVasc_Inv" = "0",
                                                      "Pos_LumphVasc_Inv" = "1",
                                                      "N_A" = "8",
                                                      "Unknown" = "9")) %>%
        mutate(RX_HOSP_SURG_APPR_2010_F = fct_recode(RX_HOSP_SURG_APPR_2010,
                                                     "No_Surg" = "0",
                                                     "Robot_Assist" = "1",
                                                     "Robot_to_Open" = "2",
                                                     "Endo_Lap" = "3",
                                                     "Endo_Lap_to_Open" = "4",
                                                     "Open_Unknown" = "5",
                                                     "Unknown" = "9")) %>%
        mutate(All = "All") %>%
        mutate(All = factor(All)) %>%
        mutate(REASON_FOR_NO_SURGERY_F = fct_recode(REASON_FOR_NO_SURGERY,
                                                    "Surg performed" = "0",
                                                    "Surg not recommended" = "1",
                                                    "No surg due to pt factors" = "2",
                                                    "No surg, pt died" = "5",
                                                    "Surg rec, not done" = "6",
                                                    "Surg rec, pt refused" = "7",
                                                    "Surg rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(SURGERY_YN = ifelse(REASON_FOR_NO_SURGERY == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_SURGERY == "9",
                                          "Ukn",
                                          "No"))) %>%
        mutate(SURG_TF = case_when(SURGERY_YN == "Yes" ~ TRUE,
                             SURGERY_YN == "No" ~ FALSE,
                             TRUE ~ NA))  %>%
        mutate(REASON_FOR_NO_RADIATION_F = fct_recode(REASON_FOR_NO_RADIATION,
                                                    "Rad performed" = "0",
                                                    "Rad not recommended" = "1",
                                                    "No Rad due to pt factors" = "2",
                                                    "No Rad, pt died" = "5",
                                                    "Rad rec, not done" = "6",
                                                    "Rad rec, pt refused" = "7",
                                                    "Rad rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(RADIATION_YN = ifelse(REASON_FOR_NO_RADIATION == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_RADIATION == "9",
                                          NA,
                                          "No"))) %>%
        mutate(SURGRAD_SEQ_F = fct_recode(RX_SUMM_SURGRAD_SEQ,
                                                   "None or Surg or Rad" = "0",
                                                   "Rad before Surg" = "2",
                                                   "Surg before Rad" = "3",
                                                   "Rad before and after Surg" = "4",
                                                   "Intraop Rad" = "5",
                                                   "Intraop Rad plus other" = "6",
                                                   "Unknown" = "9")) %>%
        mutate(SURG_RAD_SEQ = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                     "Surg Alone",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                            "Rad Alone",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0",
                                                   "No Treatment",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2",
                                                          "Rad then Surg",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3",
                                                                 "Surg then Rad",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4",
                                                                        "Rad before and after Surg",
                                                                        "Other"))))))) %>%
        mutate(SURG_RAD_SEQ = fct_relevel(SURG_RAD_SEQ,
                                          "Surg Alone",
                                          "Surg then Rad",
                                          "Rad Alone")) %>%
        mutate(CHEMO_YN = fct_collapse(RX_SUMM_CHEMO,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01", "02", "03"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(SURG_RAD_SEQ_C = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                     "Surg, No rad, No Chemo",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                            "Rad, No Surg, No Chemo",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                                   "No Surg, No Rad, No Chemo",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "No",
                                                          "Rad then Surg, No Chemo",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "No",
                                                                 "Surg then Rad, No Chemo",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "No",
                                                                        "Rad before and after Surg, No Chemo",
                                ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                       "Surg, No rad, Yes Chemo",
                                       ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                              "Rad, No Surg, Yes Chemo",
                                              ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                                     "No Surg, No Rad, Yes Chemo",
                                                     ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "Yes",
                                                            "Rad then Surg, Yes Chemo",
                                                            ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "Yes",
                                                                   "Surg then Rad, Yes Chemo",
                                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "Yes",
                                                                          "Rad before and after Surg, Yes Chemo",
                                                                          "Other"))))))))))))) %>%
        mutate(SURG_RAD_SEQ_C = fct_infreq(SURG_RAD_SEQ_C)) %>%
        mutate(T_SIZE = as.numeric(TUMOR_SIZE)) %>%
        mutate(T_SIZE = ifelse(T_SIZE == 0,
                                "No Tumor",
                                ifelse(T_SIZE > 0 & T_SIZE < 10 | T_SIZE == 991,
                                       "< 1 cm",
                                       ifelse(T_SIZE >= 10 & T_SIZE < 20 | T_SIZE == 992,
                                              "1-2 cm",
                                              ifelse(T_SIZE >= 20 & T_SIZE < 30 | T_SIZE == 993,
                                                     "2-3 cm",
                                                     ifelse(T_SIZE >= 30 & T_SIZE < 40 | T_SIZE == 994,
                                                            "3-4 cm",
                                                            ifelse(T_SIZE >= 40 & T_SIZE < 50 | T_SIZE == 995,
                                                                   "4-5 cm",
                                                                   ifelse(T_SIZE >= 50 & T_SIZE < 60 | T_SIZE == 996,
                                                                          "5-6 cm",
                                                                          ifelse(T_SIZE >= 60 & T_SIZE <= 987 |
                                                                                         T_SIZE == 980 | T_SIZE == 989 |
                                                                                         T_SIZE == 997,
                                                                          ">6 cm",
                                                                          ifelse(T_SIZE == 988 | T_SIZE == 999,
                                                                                 "NA_unk",
                                                                                 "Microscopic focus")))))))))) %>%
        mutate(T_SIZE = factor(T_SIZE)) %>%
        mutate(T_SIZE = fct_relevel(T_SIZE,
                                     "No Tumor", "Microscopic focus", "< 1 cm", "1-2 cm", "2-3 cm", "3-4 cm",
                                       "4-5 cm", "5-6 cm", ">6 cm", "NA_unk")) %>%
        mutate(mets_at_dx = case_when(CS_METS_DX_LUNG == "1" ~ "Lung",
                                      CS_METS_DX_BONE == "1" ~ "Bone",
                                      CS_METS_DX_BRAIN == "1" ~ "Brain",
                                      CS_METS_DX_LIVER == "1" ~ "Liver",
                                      TRUE ~ "None/Other/Unk/NA")) %>%
        mutate(MEDICAID_EXPN_CODE = fct_recode(MEDICAID_EXPN_CODE,
                                               "Non-Expansion State" = "0",
                                               "Jan 2014 Expansion States" = "1",
                                               "Early Expansion States (2010-13)" = "2",
                                               "Late Expansion States (> Jan 2014)" = "3",
                                               "Suppressed for Ages 0 - 39" = "9"))  %>%
        mutate(EXPN_GROUP =  case_when(MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Post-Expansion",
                                       
                                       MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% 
                                          c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013") ~ "Pre-Expansion",
               
                                       MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2010", "2011", "2012", "2013", "2014", "2015") ~ "Post-Expansion",
                                       
                                        MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", "2009") ~ "Pre-Expansion",
                                       MEDICAID_EXPN_CODE %in% c("Non-Expansion State") ~ "Pre-Expansion",
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") ~ "Pre-Expansion",
                    
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") & 
                                        YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Exclude",
                                       
                                       MEDICAID_EXPN_CODE == "Suppressed for Ages 0 - 39" ~ "Exclude")) %>%
  
  mutate(pre_2014 = YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013")) %>%
  
  mutate(mets_at_dx_F = ifelse(mets_at_dx == "None/Other/Unk/NA", FALSE, TRUE)) %>% 
  
  mutate(Tx_YN = ifelse(SURG_RAD_SEQ == "No Treatment" & CHEMO_YN == "No", FALSE, 
                        ifelse(CHEMO_YN == "Ukn", NA, 
                               TRUE)))
fact_vars_2 <- c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "AGE_F", "SEX_F", "RACE_F",
                 "HISPANIC", "INSURANCE_F", "INCOME_F", "EDUCATION_F", "U_R_F",
                 "CDCC_TOTAL_BEST", "CLASS_OF_CASE_F", "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "HISTOLOGY",
                 "BEHAVIOR", "GRADE_F", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M", "TNM_PATH_STAGE_GROUP",
                 "MARGINS", "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "mets_at_dx")
dat <- dat %>%
        mutate_at(fact_vars_2, funs(factor(.)))

functions

p_table <- function(tab_data, ...) {
  tab_data_2 <- deparse(substitute(tab_data))
  
  table_p <- do.call(CreateTableOne, 
                     list(data = as.name(tab_data_2), includeNA = TRUE, ...))
  table_p_out <- print(table_p,
                       showAllLevels = TRUE,
                       printToggle = FALSE)
  kable(table_p_out,
        align = "c")
}
uni_var <- function(test_var, data_imp) {
                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")
        
        f <- as.formula(paste("Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)",
                              as.name(test_var),
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))
        km_fit <- do.call("survfit", list(formula = f, data = as.name(data_imp_2)))
        print(km_fit)
        cat("\n")
        print(summary(km_fit, times = c(12, 24, 36, 48, 60, 120)))
        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Univariable Cox Proportional Hazard Model for: ", test_var)
        cat("\n")
        cat("\n")
        n_levels <- nlevels(data_imp[[test_var]])
        if(n_levels == 1){
                print("Only one level, no Cox model performed")
                cat("\n")
        } else {
                cox_fit <- do.call("coxph", list(formula = f, data = as.name(data_imp_2)))
                print(summary(cox_fit))
                cat("\n")
                
                do.call("ggforest",
                         list(model = cox_fit, data = as.name(data_imp_2)))
        }
        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Unadjusted Kaplan Meier Overall Survival Curve for: ", test_var)
        p <- do.call("ggsurvplot",
                     list(fit = km_fit, data = as.name(data_imp_2),
                          palette = "jco", censor = FALSE, legend = "right",
                          linetype = "strata", xlab = "Time (Months)"))
        print(p)
}

Extract Data of Interest

# Melanoma
site_code <- c("C440", "C441", "C442", "C443", "C444", "C445", "C446", "C447", "C448", "C449", "C510", "C511", "C512", "C518", "C519", "C600", "C601", "C602", "C608", "C609", "C632")
histo_code <- c("8720", "8741", "8746", "8721", "8722", "8723", "8730", "8740", "8742", "8743", "8744", "8745", "8761")
behavior_code <- c("3")
data <- dat %>%
        filter(BEHAVIOR %in% behavior_code) %>%
        filter(PRIMARY_SITE %in% site_code) %>%
        filter(HISTOLOGY %in% histo_code) %>%
        filter(is.na(PUF_VITAL_STATUS) == FALSE) %>%
        filter(is.na(DX_LASTCONTACT_DEATH_MONTHS) == FALSE) %>%
        filter(INSURANCE_STATUS %in% c("0", "1", "2", "3", "4"))
   # filter(AGE >= 18) %>%
   #      filter(TNM_CLIN_M %in% c("c0")) %>%
   #      filter(SEQUENCE_NUMBER == "00") %>%
   #      filter(CLASS_OF_CASE %in% c("10", "12", "14", "22")) %>%
        
no_Excludes <- as.data.frame(data %>% 
                               filter(EXPN_GROUP != "Exclude") 
                             %>% droplevels())
file_path <- c("/Users/beastatlife/Google Drive/Penn/Research/Barbieri/NCDB")
save(data,
      file = paste0(file_path, "/melanoma_data.Rda"))
#load("melanoma_data.Rda")

Data including skin tumors was obtained from the NCBD on October 7, 2019. Cases that were included in this analysis were those with:

  1. Site codes: C440, C441, C442, C443, C444, C445, C446, C447, C448, C449, C510, C511, C512, C518, C519, C600, C601, C602, C608, C609, C632
  2. Histology codes: 8720, 8741, 8746, 8721, 8722, 8723, 8730, 8740, 8742, 8743, 8744, 8745, 8761
  3. Behavior codes: 3

Patients were excluded if they didn’t have values for either follow up or vital status.

Patients were excluded if they had surgery to a distant site using RX_SUMM_SURG_OTH_REGDIS. This was done to avoid confounding of different surgical procedures. We are only interested in surgery at the primary site. These distant site surgeries were being counted in the surgery/radiation sequence and thus to simplify analysis they were removed.

data %>%
        CreateTableOne(data = .,
                     vars = c("RX_SUMM_SURG_OTH_REGDIS"),
                     includeNA = TRUE) %>%
        print(.,
              showAllLevels = TRUE)
                             
                              level Overall       
  n                                 379702        
  RX_SUMM_SURG_OTH_REGDIS (%) 0     362910 (95.6) 
                              1       5561 ( 1.5) 
                              2       4012 ( 1.1) 
                              3        951 ( 0.3) 
                              4       4912 ( 1.3) 
                              5        880 ( 0.2) 
                              9        476 ( 0.1) 
data <- data %>%
        filter(RX_SUMM_SURG_OTH_REGDIS == "0") 

Race was grouped as white, black, asian, other/unknown Stage was grouped into 0, I, II, III, IV, NA_Unknown, stage 0 was removed Whether surgery was performed was based on the REASON_FOR_NO_SURGERY variable. The SURGERY_YN variable was classified as ‘Yes’, ‘No’, or ‘Unknown’.

Whether radiation was performed was based on the REASON_FOR_NO_RADIATION variable. The RADIATION_YN variable was classified as ‘Yes’, ‘No’, or ‘Unknown’.

Table of variables for all cases:

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "PRIMARY_SITE", "HISTOLOGY_F_LIM", "HISTOLOGY_F", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE", "EXPN_GROUP", "SITE_TEXT"))

level Overall
n 362910
FACILITY_TYPE_F (%) Community Cancer Program 20025 ( 5.5)
Comprehensive Comm Ca Program 114916 ( 31.7)
Academic/Research Program 147602 ( 40.7)
Integrated Network Ca Program 38530 ( 10.6)
NA 41837 ( 11.5)
FACILITY_LOCATION_F (%) New England 21270 ( 5.9)
Middle Atlantic 52367 ( 14.4)
South Atlantic 72796 ( 20.1)
East North Central 53630 ( 14.8)
East South Central 21009 ( 5.8)
West North Central 27272 ( 7.5)
West South Central 16025 ( 4.4)
Mountain 17133 ( 4.7)
Pacific 39571 ( 10.9)
NA 41837 ( 11.5)
FACILITY_GEOGRAPHY (%) Northeast 73637 ( 20.3)
South 88821 ( 24.5)
Midwest 101911 ( 28.1)
West 56704 ( 15.6)
NA 41837 ( 11.5)
AGE (mean (sd)) 60.69 (16.46)
AGE_F (%) (0,54] 123853 ( 34.1)
(54,64] 79330 ( 21.9)
(64,74] 76938 ( 21.2)
(74,100] 82755 ( 22.8)
NA 34 ( 0.0)
AGE_40 (%) (0,40] 45430 ( 12.5)
(40,100] 317446 ( 87.5)
NA 34 ( 0.0)
SEX_F (%) Male 207765 ( 57.2)
Female 155145 ( 42.8)
RACE_F (%) White 353647 ( 97.4)
Black 2116 ( 0.6)
Other/Unk 6088 ( 1.7)
Asian 1059 ( 0.3)
HISPANIC (%) No 338276 ( 93.2)
Yes 5052 ( 1.4)
Unknown 19582 ( 5.4)
INSURANCE_F (%) Private 196431 ( 54.1)
None 8903 ( 2.5)
Medicaid 9359 ( 2.6)
Medicare 144437 ( 39.8)
Other Government 3780 ( 1.0)
Unknown 0 ( 0.0)
INCOME_F (%) Less than $38,000 39086 ( 10.8)
$38,000 - $47,999 74009 ( 20.4)
$48,000 - $62,999 98845 ( 27.2)
$63,000 + 149259 ( 41.1)
NA 1711 ( 0.5)
EDUCATION_F (%) 21% or more 36157 ( 10.0)
13 - 20.9% 77301 ( 21.3)
7 - 12.9% 126598 ( 34.9)
Less than 7% 121364 ( 33.4)
NA 1490 ( 0.4)
U_R_F (%) Metro 297888 ( 82.1)
Urban 47786 ( 13.2)
Rural 6199 ( 1.7)
NA 11037 ( 3.0)
CROWFLY (mean (sd)) 32.67 (107.89)
CDCC_TOTAL_BEST (%) 0 313112 ( 86.3)
1 39439 ( 10.9)
2 7684 ( 2.1)
3 2675 ( 0.7)
PRIMARY_SITE (%) C000 0 ( 0.0)
C001 0 ( 0.0)
C002 0 ( 0.0)
C003 0 ( 0.0)
C004 0 ( 0.0)
C005 0 ( 0.0)
C006 0 ( 0.0)
C008 0 ( 0.0)
C009 0 ( 0.0)
C019 0 ( 0.0)
C024 0 ( 0.0)
C029 0 ( 0.0)
C031 0 ( 0.0)
C050 0 ( 0.0)
C051 0 ( 0.0)
C059 0 ( 0.0)
C060 0 ( 0.0)
C069 0 ( 0.0)
C079 0 ( 0.0)
C080 0 ( 0.0)
C089 0 ( 0.0)
C090 0 ( 0.0)
C091 0 ( 0.0)
C098 0 ( 0.0)
C099 0 ( 0.0)
C100 0 ( 0.0)
C108 0 ( 0.0)
C109 0 ( 0.0)
C111 0 ( 0.0)
C113 0 ( 0.0)
C118 0 ( 0.0)
C119 0 ( 0.0)
C129 0 ( 0.0)
C131 0 ( 0.0)
C140 0 ( 0.0)
C142 0 ( 0.0)
C148 0 ( 0.0)
C154 0 ( 0.0)
C159 0 ( 0.0)
C160 0 ( 0.0)
C161 0 ( 0.0)
C162 0 ( 0.0)
C163 0 ( 0.0)
C166 0 ( 0.0)
C168 0 ( 0.0)
C169 0 ( 0.0)
C170 0 ( 0.0)
C171 0 ( 0.0)
C172 0 ( 0.0)
C179 0 ( 0.0)
C180 0 ( 0.0)
C181 0 ( 0.0)
C182 0 ( 0.0)
C184 0 ( 0.0)
C186 0 ( 0.0)
C187 0 ( 0.0)
C188 0 ( 0.0)
C189 0 ( 0.0)
C199 0 ( 0.0)
C209 0 ( 0.0)
C210 0 ( 0.0)
C211 0 ( 0.0)
C218 0 ( 0.0)
C220 0 ( 0.0)
C221 0 ( 0.0)
C239 0 ( 0.0)
C240 0 ( 0.0)
C250 0 ( 0.0)
C252 0 ( 0.0)
C257 0 ( 0.0)
C258 0 ( 0.0)
C259 0 ( 0.0)
C268 0 ( 0.0)
C269 0 ( 0.0)
C300 0 ( 0.0)
C310 0 ( 0.0)
C321 0 ( 0.0)
C339 0 ( 0.0)
C340 0 ( 0.0)
C341 0 ( 0.0)
C342 0 ( 0.0)
C343 0 ( 0.0)
C348 0 ( 0.0)
C349 0 ( 0.0)
C379 0 ( 0.0)
C380 0 ( 0.0)
C381 0 ( 0.0)
C382 0 ( 0.0)
C383 0 ( 0.0)
C384 0 ( 0.0)
C388 0 ( 0.0)
C400 0 ( 0.0)
C402 0 ( 0.0)
C410 0 ( 0.0)
C412 0 ( 0.0)
C413 0 ( 0.0)
C414 0 ( 0.0)
C419 0 ( 0.0)
C420 0 ( 0.0)
C421 0 ( 0.0)
C422 0 ( 0.0)
C424 0 ( 0.0)
C440 631 ( 0.2)
C441 1184 ( 0.3)
C442 10639 ( 2.9)
C443 33680 ( 9.3)
C444 30276 ( 8.3)
C445 113276 ( 31.2)
C446 90064 ( 24.8)
C447 67737 ( 18.7)
C448 389 ( 0.1)
C449 15034 ( 4.1)
C480 0 ( 0.0)
C481 0 ( 0.0)
C482 0 ( 0.0)
C488 0 ( 0.0)
C490 0 ( 0.0)
C491 0 ( 0.0)
C492 0 ( 0.0)
C493 0 ( 0.0)
C494 0 ( 0.0)
C495 0 ( 0.0)
C496 0 ( 0.0)
C499 0 ( 0.0)
C502 0 ( 0.0)
C503 0 ( 0.0)
C504 0 ( 0.0)
C505 0 ( 0.0)
C506 0 ( 0.0)
C508 0 ( 0.0)
C509 0 ( 0.0)
C529 0 ( 0.0)
C559 0 ( 0.0)
C579 0 ( 0.0)
C629 0 ( 0.0)
C649 0 ( 0.0)
C669 0 ( 0.0)
C678 0 ( 0.0)
C679 0 ( 0.0)
C690 0 ( 0.0)
C695 0 ( 0.0)
C696 0 ( 0.0)
C701 0 ( 0.0)
C709 0 ( 0.0)
C710 0 ( 0.0)
C711 0 ( 0.0)
C712 0 ( 0.0)
C713 0 ( 0.0)
C714 0 ( 0.0)
C715 0 ( 0.0)
C716 0 ( 0.0)
C719 0 ( 0.0)
C720 0 ( 0.0)
C729 0 ( 0.0)
C739 0 ( 0.0)
C749 0 ( 0.0)
C760 0 ( 0.0)
C761 0 ( 0.0)
C762 0 ( 0.0)
C763 0 ( 0.0)
C764 0 ( 0.0)
C765 0 ( 0.0)
C770 0 ( 0.0)
C771 0 ( 0.0)
C772 0 ( 0.0)
C773 0 ( 0.0)
C774 0 ( 0.0)
C775 0 ( 0.0)
C778 0 ( 0.0)
C779 0 ( 0.0)
C809 0 ( 0.0)
HISTOLOGY_F_LIM (%) 8720 182616 ( 50.3)
9680 0 ( 0.0)
8743 112470 ( 31.0)
8742 19251 ( 5.3)
Other 48573 ( 13.4)
HISTOLOGY_F (%) 8720 182616 ( 50.3)
9680 0 ( 0.0)
8743 112470 ( 31.0)
8742 19251 ( 5.3)
8721 34314 ( 9.5)
9663 0 ( 0.0)
9690 0 ( 0.0)
9591 0 ( 0.0)
9691 0 ( 0.0)
9650 0 ( 0.0)
9695 0 ( 0.0)
9673 0 ( 0.0)
9699 0 ( 0.0)
8247 0 ( 0.0)
9698 0 ( 0.0)
9823 0 ( 0.0)
9670 0 ( 0.0)
9590 0 ( 0.0)
8070 0 ( 0.0)
9702 0 ( 0.0)
9652 0 ( 0.0)
9687 0 ( 0.0)
8745 5612 ( 1.5)
8832 0 ( 0.0)
8744 4796 ( 1.3)
8772 0 ( 0.0)
9714 0 ( 0.0)
9689 0 ( 0.0)
9659 0 ( 0.0)
9705 0 ( 0.0)
9671 0 ( 0.0)
8410 0 ( 0.0)
9651 0 ( 0.0)
9596 0 ( 0.0)
8730 1330 ( 0.4)
8723 1293 ( 0.4)
9837 0 ( 0.0)
9120 0 ( 0.0)
8830 0 ( 0.0)
8071 0 ( 0.0)
8771 0 ( 0.0)
9665 0 ( 0.0)
8770 0 ( 0.0)
8761 790 ( 0.2)
8409 0 ( 0.0)
9729 0 ( 0.0)
9684 0 ( 0.0)
8890 0 ( 0.0)
9653 0 ( 0.0)
8542 0 ( 0.0)
9667 0 ( 0.0)
8390 0 ( 0.0)
8407 0 ( 0.0)
8413 0 ( 0.0)
9727 0 ( 0.0)
8200 0 ( 0.0)
8740 286 ( 0.1)
8480 0 ( 0.0)
9735 0 ( 0.0)
9679 0 ( 0.0)
8402 0 ( 0.0)
9811 0 ( 0.0)
8140 0 ( 0.0)
9675 0 ( 0.0)
8090 0 ( 0.0)
8802 0 ( 0.0)
8400 0 ( 0.0)
9716 0 ( 0.0)
9827 0 ( 0.0)
8401 0 ( 0.0)
8408 0 ( 0.0)
9719 0 ( 0.0)
9664 0 ( 0.0)
8430 0 ( 0.0)
9728 0 ( 0.0)
8800 0 ( 0.0)
8097 0 ( 0.0)
8833 0 ( 0.0)
8051 0 ( 0.0)
8246 0 ( 0.0)
8801 0 ( 0.0)
8403 0 ( 0.0)
9678 0 ( 0.0)
8560 0 ( 0.0)
8722 91 ( 0.0)
9738 0 ( 0.0)
8010 0 ( 0.0)
8980 0 ( 0.0)
8780 0 ( 0.0)
8525 0 ( 0.0)
8143 0 ( 0.0)
9717 0 ( 0.0)
8940 0 ( 0.0)
8260 0 ( 0.0)
8094 0 ( 0.0)
8746 48 ( 0.0)
8075 0 ( 0.0)
8805 0 ( 0.0)
8811 0 ( 0.0)
8804 0 ( 0.0)
8810 0 ( 0.0)
9655 0 ( 0.0)
8123 0 ( 0.0)
8310 0 ( 0.0)
9709 0 ( 0.0)
8481 0 ( 0.0)
8074 0 ( 0.0)
8982 0 ( 0.0)
8147 0 ( 0.0)
8072 0 ( 0.0)
9737 0 ( 0.0)
9708 0 ( 0.0)
8550 0 ( 0.0)
8420 0 ( 0.0)
8891 0 ( 0.0)
8500 0 ( 0.0)
8083 0 ( 0.0)
8741 13 ( 0.0)
9724 0 ( 0.0)
8076 0 ( 0.0)
8850 0 ( 0.0)
9718 0 ( 0.0)
9700 0 ( 0.0)
8255 0 ( 0.0)
8854 0 ( 0.0)
8081 0 ( 0.0)
8092 0 ( 0.0)
8852 0 ( 0.0)
9540 0 ( 0.0)
8052 0 ( 0.0)
8562 0 ( 0.0)
8711 0 ( 0.0)
9044 0 ( 0.0)
8490 0 ( 0.0)
9580 0 ( 0.0)
9654 0 ( 0.0)
8000 0 ( 0.0)
9133 0 ( 0.0)
8851 0 ( 0.0)
9260 0 ( 0.0)
9701 0 ( 0.0)
8502 0 ( 0.0)
9812 0 ( 0.0)
8032 0 ( 0.0)
8091 0 ( 0.0)
9560 0 ( 0.0)
8440 0 ( 0.0)
8575 0 ( 0.0)
9130 0 ( 0.0)
8406 0 ( 0.0)
8774 0 ( 0.0)
8894 0 ( 0.0)
8910 0 ( 0.0)
8920 0 ( 0.0)
8201 0 ( 0.0)
8240 0 ( 0.0)
8450 0 ( 0.0)
8540 0 ( 0.0)
8574 0 ( 0.0)
8803 0 ( 0.0)
9473 0 ( 0.0)
9597 0 ( 0.0)
9662 0 ( 0.0)
8033 0 ( 0.0)
8073 0 ( 0.0)
8084 0 ( 0.0)
8773 0 ( 0.0)
8815 0 ( 0.0)
8858 0 ( 0.0)
8840 0 ( 0.0)
8900 0 ( 0.0)
8963 0 ( 0.0)
9150 0 ( 0.0)
9180 0 ( 0.0)
9220 0 ( 0.0)
9726 0 ( 0.0)
9814 0 ( 0.0)
9816 0 ( 0.0)
9817 0 ( 0.0)
8050 0 ( 0.0)
8124 0 ( 0.0)
8230 0 ( 0.0)
8249 0 ( 0.0)
8263 0 ( 0.0)
8320 0 ( 0.0)
8323 0 ( 0.0)
8470 0 ( 0.0)
8520 0 ( 0.0)
8573 0 ( 0.0)
8726 0 ( 0.0)
8825 0 ( 0.0)
8896 0 ( 0.0)
9364 0 ( 0.0)
9813 0 ( 0.0)
9815 0 ( 0.0)
8004 0 ( 0.0)
8022 0 ( 0.0)
8078 0 ( 0.0)
8095 0 ( 0.0)
8098 0 ( 0.0)
8190 0 ( 0.0)
8290 0 ( 0.0)
8340 0 ( 0.0)
8341 0 ( 0.0)
8523 0 ( 0.0)
8543 0 ( 0.0)
8710 0 ( 0.0)
8760 0 ( 0.0)
8806 0 ( 0.0)
8835 0 ( 0.0)
9041 0 ( 0.0)
9043 0 ( 0.0)
9170 0 ( 0.0)
9661 0 ( 0.0)
9818 0 ( 0.0)
8020 0 ( 0.0)
8041 0 ( 0.0)
8082 0 ( 0.0)
8102 0 ( 0.0)
8110 0 ( 0.0)
8120 0 ( 0.0)
8121 0 ( 0.0)
8211 0 ( 0.0)
8245 0 ( 0.0)
8270 0 ( 0.0)
8312 0 ( 0.0)
8332 0 ( 0.0)
8347 0 ( 0.0)
8441 0 ( 0.0)
8471 0 ( 0.0)
8503 0 ( 0.0)
8524 0 ( 0.0)
8570 0 ( 0.0)
8583 0 ( 0.0)
8750 0 ( 0.0)
8823 0 ( 0.0)
8831 0 ( 0.0)
8836 0 ( 0.0)
8855 0 ( 0.0)
8936 0 ( 0.0)
8941 0 ( 0.0)
8990 0 ( 0.0)
9000 0 ( 0.0)
9020 0 ( 0.0)
9040 0 ( 0.0)
9080 0 ( 0.0)
9102 0 ( 0.0)
9105 0 ( 0.0)
9451 0 ( 0.0)
9530 0 ( 0.0)
9561 0 ( 0.0)
BEHAVIOR (%) 2 0 ( 0.0)
3 362910 (100.0)
GRADE_F (%) Gr I: Well Diff 759 ( 0.2)
Gr II: Mod Diff 1064 ( 0.3)
Gr III: Poor Diff 1877 ( 0.5)
Gr IV: Undiff/Anaplastic 665 ( 0.2)
5 0 ( 0.0)
6 0 ( 0.0)
7 0 ( 0.0)
8 0 ( 0.0)
NA/Unkown 358545 ( 98.8)
DX_STAGING_PROC_DAYS (mean (sd)) 3.39 (54.84)
TNM_CLIN_T (%) N_A 1608 ( 0.4)
c0 4261 ( 1.2)
c1 18911 ( 5.2)
c1A 55175 ( 15.2)
c1B 20634 ( 5.7)
c2 5403 ( 1.5)
c2A 34068 ( 9.4)
c2B 8717 ( 2.4)
c2C 0 ( 0.0)
c3 3178 ( 0.9)
c3A 13377 ( 3.7)
c3B 10395 ( 2.9)
c4 2170 ( 0.6)
c4A 6281 ( 1.7)
c4B 10655 ( 2.9)
cX 155744 ( 42.9)
pIS 2539 ( 0.7)
NA 9794 ( 2.7)
TNM_CLIN_N (%) N_A 1608 ( 0.4)
c0 247096 ( 68.1)
c1 5666 ( 1.6)
c1A 1325 ( 0.4)
c1B 1624 ( 0.4)
c2 1148 ( 0.3)
c2A 415 ( 0.1)
c2B 805 ( 0.2)
c2C 900 ( 0.2)
c3 2603 ( 0.7)
cX 91422 ( 25.2)
NA 8298 ( 2.3)
TNM_CLIN_M (%) N_A 1609 ( 0.4)
c0 331754 ( 91.4)
c1 4393 ( 1.2)
c1A 1449 ( 0.4)
c1B 1929 ( 0.5)
c1C 5406 ( 1.5)
NA 16370 ( 4.5)
TNM_CLIN_STAGE_GROUP (%) 0 3415 ( 0.9)
1 11136 ( 3.1)
1A 95569 ( 26.3)
1B 63589 ( 17.5)
1C 0 ( 0.0)
2 1978 ( 0.5)
2A 22432 ( 6.2)
2B 15534 ( 4.3)
2C 7948 ( 2.2)
3 11303 ( 3.1)
3A 0 ( 0.0)
3B 0 ( 0.0)
3C 0 ( 0.0)
4 13516 ( 3.7)
4A 0 ( 0.0)
4B 0 ( 0.0)
4C 0 ( 0.0)
N_A 1609 ( 0.4)
99 114838 ( 31.6)
NA 43 ( 0.0)
TNM_PATH_T (%) N_A 1608 ( 0.4)
p0 4729 ( 1.3)
p1 12061 ( 3.3)
p1A 60788 ( 16.8)
p1B 23363 ( 6.4)
p2 3707 ( 1.0)
p2A 40964 ( 11.3)
p2B 9316 ( 2.6)
p3 2393 ( 0.7)
p3A 17370 ( 4.8)
p3B 12918 ( 3.6)
p4 1628 ( 0.4)
p4A 9301 ( 2.6)
p4B 15277 ( 4.2)
pIS 1358 ( 0.4)
pX 130955 ( 36.1)
NA 15174 ( 4.2)
TNM_PATH_N (%) N_A 1608 ( 0.4)
p0 161766 ( 44.6)
p1 4601 ( 1.3)
p1A 10736 ( 3.0)
p1B 2516 ( 0.7)
p2 1370 ( 0.4)
p2A 3523 ( 1.0)
p2B 1858 ( 0.5)
p2C 1798 ( 0.5)
p3 4889 ( 1.3)
pX 139376 ( 38.4)
NA 28869 ( 8.0)
TNM_PATH_M (%) N_A 1609 ( 0.4)
p1 1989 ( 0.5)
p1A 1165 ( 0.3)
p1B 907 ( 0.2)
p1C 2241 ( 0.6)
pX 155551 ( 42.9)
NA 199448 ( 55.0)
TNM_PATH_STAGE_GROUP (%) 0 2692 ( 0.7)
1 8007 ( 2.2)
1A 86795 ( 23.9)
1B 70294 ( 19.4)
1C 0 ( 0.0)
2 1642 ( 0.5)
2A 24145 ( 6.7)
2B 16599 ( 4.6)
2C 8258 ( 2.3)
3 6964 ( 1.9)
3A 10302 ( 2.8)
3B 9390 ( 2.6)
3C 6606 ( 1.8)
4 7652 ( 2.1)
4A 0 ( 0.0)
4B 0 ( 0.0)
4C 0 ( 0.0)
N_A 1609 ( 0.4)
99 90809 ( 25.0)
NA 11146 ( 3.1)
DX_RX_STARTED_DAYS (mean (sd)) 11.01 (28.65)
DX_SURG_STARTED_DAYS (mean (sd)) 10.38 (27.55)
DX_DEFSURG_STARTED_DAYS (mean (sd)) 31.34 (35.58)
MARGINS (%) No Residual 329265 ( 90.7)
Residual, NOS 5326 ( 1.5)
Microscopic Resid 5723 ( 1.6)
Macroscopic Resid 366 ( 0.1)
Not evaluable 999 ( 0.3)
No surg 16733 ( 4.6)
Unknown 4498 ( 1.2)
MARGINS_YN (%) No 329265 ( 90.7)
Yes 11415 ( 3.1)
No surg/Unk/NA 22230 ( 6.1)
SURG_DISCHARGE_DAYS (mean (sd)) 1.74 (9.14)
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 347736 ( 95.8)
Unplan_Readmit_Same 3407 ( 0.9)
Plan_Readmit_Same 5388 ( 1.5)
PlanUnplan_Same 515 ( 0.1)
9 5864 ( 1.6)
RX_SUMM_RADIATION_F (%) None 349080 ( 96.2)
Beam Radiation 11458 ( 3.2)
Radioactive Implants 44 ( 0.0)
Radioisotopes 9 ( 0.0)
Beam + Imp or Isotopes 9 ( 0.0)
Radiation, NOS 114 ( 0.0)
Unknown 2196 ( 0.6)
PUF_30_DAY_MORT_CD_F (%) Alive_30 337013 ( 92.9)
Dead_30 792 ( 0.2)
Unknown 8120 ( 2.2)
NA 16985 ( 4.7)
PUF_90_DAY_MORT_CD_F (%) Alive_90 329604 ( 90.8)
Dead_90 2646 ( 0.7)
Unknown 13675 ( 3.8)
NA 16985 ( 4.7)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 55.24 (41.17)
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 145065 ( 40.0)
Pos_LumphVasc_Inv 7694 ( 2.1)
N_A 50 ( 0.0)
Unknown 50201 ( 13.8)
NA 159900 ( 44.1)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 18689 ( 5.1)
Robot_Assist 148 ( 0.0)
Robot_to_Open 37 ( 0.0)
Endo_Lap 441 ( 0.1)
Endo_Lap_to_Open 170 ( 0.0)
Open_Unknown 183492 ( 50.6)
Unknown 33 ( 0.0)
NA 159900 ( 44.1)
SURG_RAD_SEQ (%) Surg Alone 337579 ( 93.0)
Surg then Rad 6249 ( 1.7)
Rad Alone 5240 ( 1.4)
No Treatment 10837 ( 3.0)
Other 2891 ( 0.8)
Rad before and after Surg 17 ( 0.0)
Rad then Surg 97 ( 0.0)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 324878 ( 89.5)
No Surg, No Rad, Yes Chemo 2269 ( 0.6)
No Surg, No Rad, No Chemo 8215 ( 2.3)
Surg, No rad, Yes Chemo 4027 ( 1.1)
Other 12228 ( 3.4)
Rad, No Surg, Yes Chemo 1859 ( 0.5)
Surg then Rad, No Chemo 5156 ( 1.4)
Surg then Rad, Yes Chemo 906 ( 0.2)
Rad, No Surg, No Chemo 3258 ( 0.9)
Rad then Surg, No Chemo 66 ( 0.0)
Rad then Surg, Yes Chemo 31 ( 0.0)
Rad before and after Surg, No Chemo 14 ( 0.0)
Rad before and after Surg, Yes Chemo 3 ( 0.0)
SURGERY_YN (%) No 16214 ( 4.5)
Ukn 684 ( 0.2)
Yes 346012 ( 95.3)
RADIATION_YN (%) No 348906 ( 96.1)
Yes 11634 ( 3.2)
NA 2370 ( 0.7)
CHEMO_YN (%) No 342669 ( 94.4)
Yes 9216 ( 2.5)
Ukn 11025 ( 3.0)
mets_at_dx (%) Bone 763 ( 0.2)
Brain 886 ( 0.2)
Liver 592 ( 0.2)
Lung 3474 ( 1.0)
None/Other/Unk/NA 357195 ( 98.4)
MEDICAID_EXPN_CODE (%) Non-Expansion State 114831 ( 31.6)
Jan 2014 Expansion States 100097 ( 27.6)
Early Expansion States (2010-13) 61288 ( 16.9)
Late Expansion States (> Jan 2014) 44857 ( 12.4)
Suppressed for Ages 0 - 39 41837 ( 11.5)
EXPN_GROUP (%) Exclude 41837 ( 11.5)
Post-Expansion 55764 ( 15.4)
Pre-Expansion 265309 ( 73.1)
SITE_TEXT (%) C44.0 Skin of lip, NOS 631 ( 0.2)
C44.1 Eyelid 1184 ( 0.3)
C44.2 External ear 10639 ( 2.9)
C44.3 Skin of ear and unspecified parts of face 33680 ( 9.3)
C44.4 Skin of scalp and neck 30276 ( 8.3)
C44.5 Skin of trunk 113276 ( 31.2)
C44.6 Skin of upper limb and shoulder 90064 ( 24.8)
C44.7 Skin of lower limb and hip 67737 ( 18.7)
C44.8 Overlapping lesion of skin 389 ( 0.1)
C44.9 Skin, NOS 15034 ( 4.1)

p_table(no_Excludes,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "PRIMARY_SITE", "HISTOLOGY_F_LIM", "HISTOLOGY_F", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE","SITE_TEXT"), 
        strata = "EXPN_GROUP")
These variables only have NA/NaN: T_SIZE  Dropped

level Post-Expansion Pre-Expansion p test
n 57862 278211
FACILITY_TYPE_F (%) Community Cancer Program 3221 ( 5.6) 17611 ( 6.3) <0.001
Comprehensive Comm Ca Program 19026 ( 32.9) 100775 ( 36.2)
Academic/Research Program 31135 ( 53.8) 123481 ( 44.4)
Integrated Network Ca Program 4480 ( 7.7) 36344 ( 13.1)
FACILITY_LOCATION_F (%) New England 5930 ( 10.2) 16209 ( 5.8) <0.001
Middle Atlantic 12096 ( 20.9) 42501 ( 15.3)
South Atlantic 2410 ( 4.2) 74171 ( 26.7)
East North Central 5811 ( 10.0) 50106 ( 18.0)
East South Central 1343 ( 2.3) 21004 ( 7.5)
West North Central 7194 ( 12.4) 21268 ( 7.6)
West South Central 127 ( 0.2) 16744 ( 6.0)
Mountain 2479 ( 4.3) 15535 ( 5.6)
Pacific 20472 ( 35.4) 20673 ( 7.4)
FACILITY_GEOGRAPHY (%) Northeast 18026 ( 31.2) 58710 ( 21.1) <0.001
South 2537 ( 4.4) 90915 ( 32.7)
Midwest 14348 ( 24.8) 92378 ( 33.2)
West 22951 ( 39.7) 36208 ( 13.0)
AGE (mean (sd)) 65.49 (12.79) 64.34 (13.03) <0.001
AGE_F (%) (0,54] 12889 ( 22.3) 73067 ( 26.3) <0.001
(54,64] 14728 ( 25.5) 68624 ( 24.7)
(64,74] 14815 ( 25.6) 65791 ( 23.6)
(74,100] 15430 ( 26.7) 70729 ( 25.4)
AGE_40 (%) (0,40] 509 ( 0.9) 3282 ( 1.2) <0.001
(40,100] 57353 ( 99.1) 274929 ( 98.8)
SEX_F (%) Male 34722 ( 60.0) 167707 ( 60.3) 0.225
Female 23140 ( 40.0) 110504 ( 39.7)
RACE_F (%) White 56383 ( 97.4) 271444 ( 97.6) <0.001
Black 248 ( 0.4) 1767 ( 0.6)
Other/Unk 940 ( 1.6) 4399 ( 1.6)
Asian 291 ( 0.5) 601 ( 0.2)
HISPANIC (%) No 55363 ( 95.7) 258455 ( 92.9) <0.001
Yes 1091 ( 1.9) 3305 ( 1.2)
Unknown 1408 ( 2.4) 16451 ( 5.9)
INSURANCE_F (%) Private 28513 ( 49.3) 139177 ( 50.0) <0.001
None 728 ( 1.3) 6660 ( 2.4)
Medicaid 1938 ( 3.3) 5338 ( 1.9)
Medicare 26274 ( 45.4) 124013 ( 44.6)
Other Government 409 ( 0.7) 3023 ( 1.1)
INCOME_F (%) Less than $38,000 3143 ( 5.4) 33541 ( 12.1) <0.001
$38,000 - $47,999 7627 ( 13.2) 61337 ( 22.0)
$48,000 - $62,999 14132 ( 24.4) 77116 ( 27.7)
$63,000 + 32828 ( 56.7) 104761 ( 37.7)
NA 132 ( 0.2) 1456 ( 0.5)
EDUCATION_F (%) 21% or more 4586 ( 7.9) 29264 ( 10.5) <0.001
13 - 20.9% 9266 ( 16.0) 62785 ( 22.6)
7 - 12.9% 20331 ( 35.1) 96700 ( 34.8)
Less than 7% 23572 ( 40.7) 88186 ( 31.7)
NA 107 ( 0.2) 1276 ( 0.5)
U_R_F (%) Metro 49795 ( 86.1) 225331 ( 81.0) <0.001
Urban 5798 ( 10.0) 38929 ( 14.0)
Rural 534 ( 0.9) 5387 ( 1.9)
NA 1735 ( 3.0) 8564 ( 3.1)
CROWFLY (mean (sd)) 29.28 (105.80) 33.49 (107.35) <0.001
CDCC_TOTAL_BEST (%) 0 48500 ( 83.8) 236220 ( 84.9) <0.001
1 7282 ( 12.6) 33004 ( 11.9)
2 1468 ( 2.5) 6708 ( 2.4)
3 612 ( 1.1) 2279 ( 0.8)
PRIMARY_SITE (%) C440 95 ( 0.2) 510 ( 0.2) 0.001
C441 181 ( 0.3) 975 ( 0.4)
C442 1682 ( 2.9) 8426 ( 3.0)
C443 5412 ( 9.4) 27343 ( 9.8)
C444 4881 ( 8.4) 23580 ( 8.5)
C445 17330 ( 30.0) 82506 ( 29.7)
C446 14455 ( 25.0) 68861 ( 24.8)
C447 10302 ( 17.8) 48320 ( 17.4)
C448 64 ( 0.1) 318 ( 0.1)
C449 3460 ( 6.0) 17372 ( 6.2)
HISTOLOGY_F_LIM (%) 8720 30919 ( 53.4) 141506 ( 50.9) <0.001
8743 16201 ( 28.0) 81437 ( 29.3)
8742 3050 ( 5.3) 16238 ( 5.8)
Other 7692 ( 13.3) 39030 ( 14.0)
HISTOLOGY_F (%) 8720 30919 ( 53.4) 141506 ( 50.9) <0.001
8743 16201 ( 28.0) 81437 ( 29.3)
8742 3050 ( 5.3) 16238 ( 5.8)
8721 5413 ( 9.4) 27541 ( 9.9)
8745 942 ( 1.6) 4665 ( 1.7)
8744 757 ( 1.3) 3871 ( 1.4)
8730 273 ( 0.5) 1124 ( 0.4)
8723 151 ( 0.3) 1042 ( 0.4)
8761 91 ( 0.2) 469 ( 0.2)
8740 41 ( 0.1) 195 ( 0.1)
8722 12 ( 0.0) 74 ( 0.0)
8746 9 ( 0.0) 39 ( 0.0)
8741 3 ( 0.0) 10 ( 0.0)
BEHAVIOR (%) 3 57862 (100.0) 278211 (100.0) NA
GRADE_F (%) Gr I: Well Diff 88 ( 0.2) 606 ( 0.2) <0.001
Gr II: Mod Diff 139 ( 0.2) 804 ( 0.3)
Gr III: Poor Diff 245 ( 0.4) 1688 ( 0.6)
Gr IV: Undiff/Anaplastic 127 ( 0.2) 536 ( 0.2)
NA/Unkown 57263 ( 99.0) 274577 ( 98.7)
DX_STAGING_PROC_DAYS (mean (sd)) 2.43 (22.43) 3.98 (61.22) 0.001
TNM_CLIN_T (%) N_A 4 ( 0.0) 1545 ( 0.6) <0.001
c0 1231 ( 2.1) 4801 ( 1.7)
c1 3089 ( 5.3) 13742 ( 4.9)
c1A 16108 ( 27.8) 33019 ( 11.9)
c1B 5554 ( 9.6) 12635 ( 4.5)
c2 925 ( 1.6) 4089 ( 1.5)
c2A 6115 ( 10.6) 24936 ( 9.0)
c2B 1727 ( 3.0) 6602 ( 2.4)
c3 531 ( 0.9) 2536 ( 0.9)
c3A 2494 ( 4.3) 9998 ( 3.6)
c3B 2067 ( 3.6) 8069 ( 2.9)
c4 385 ( 0.7) 1768 ( 0.6)
c4A 1120 ( 1.9) 4984 ( 1.8)
c4B 2061 ( 3.6) 8534 ( 3.1)
cX 10139 ( 17.5) 133420 ( 48.0)
pIS 563 ( 1.0) 1902 ( 0.7)
NA 3749 ( 6.5) 5631 ( 2.0)
TNM_CLIN_N (%) N_A 4 ( 0.0) 1545 ( 0.6) <0.001
c0 46329 ( 80.1) 178425 ( 64.1)
c1 1298 ( 2.2) 4595 ( 1.7)
c1A 142 ( 0.2) 1125 ( 0.4)
c1B 216 ( 0.4) 1479 ( 0.5)
c2 229 ( 0.4) 941 ( 0.3)
c2A 59 ( 0.1) 362 ( 0.1)
c2B 111 ( 0.2) 757 ( 0.3)
c2C 207 ( 0.4) 820 ( 0.3)
c3 543 ( 0.9) 2380 ( 0.9)
cX 5486 ( 9.5) 81017 ( 29.1)
NA 3238 ( 5.6) 4765 ( 1.7)
TNM_CLIN_M (%) N_A 4 ( 0.0) 1546 ( 0.6) <0.001
c0 48596 ( 84.0) 253240 ( 91.0)
c1 660 ( 1.1) 4964 ( 1.8)
c1A 382 ( 0.7) 1666 ( 0.6)
c1B 511 ( 0.9) 1937 ( 0.7)
c1C 1652 ( 2.9) 5358 ( 1.9)
NA 6057 ( 10.5) 9500 ( 3.4)
TNM_CLIN_STAGE_GROUP (%) 0 727 ( 1.3) 2588 ( 0.9) <0.001
1 1843 ( 3.2) 7997 ( 2.9)
1A 17346 ( 30.0) 66257 ( 23.8)
1B 11641 ( 20.1) 45327 ( 16.3)
2 306 ( 0.5) 1529 ( 0.5)
2A 4183 ( 7.2) 16896 ( 6.1)
2B 2900 ( 5.0) 12121 ( 4.4)
2C 1554 ( 2.7) 6284 ( 2.3)
3 2074 ( 3.6) 9546 ( 3.4)
4 3324 ( 5.7) 14210 ( 5.1)
N_A 4 ( 0.0) 1545 ( 0.6)
99 11944 ( 20.6) 93881 ( 33.7)
NA 16 ( 0.0) 30 ( 0.0)
TNM_PATH_T (%) N_A 4 ( 0.0) 1545 ( 0.6) <0.001
p0 1085 ( 1.9) 3953 ( 1.4)
p1 1747 ( 3.0) 8963 ( 3.2)
p1A 18169 ( 31.4) 36049 ( 13.0)
p1B 6406 ( 11.1) 14138 ( 5.1)
p2 469 ( 0.8) 2936 ( 1.1)
p2A 7761 ( 13.4) 29413 ( 10.6)
p2B 1824 ( 3.2) 6995 ( 2.5)
p3 345 ( 0.6) 1970 ( 0.7)
p3A 3365 ( 5.8) 12866 ( 4.6)
p3B 2637 ( 4.6) 9921 ( 3.6)
p4 263 ( 0.5) 1384 ( 0.5)
p4A 1905 ( 3.3) 7222 ( 2.6)
p4B 3249 ( 5.6) 12057 ( 4.3)
pIS 247 ( 0.4) 1064 ( 0.4)
pX 3576 ( 6.2) 117143 ( 42.1)
NA 4810 ( 8.3) 10592 ( 3.8)
TNM_PATH_N (%) N_A 4 ( 0.0) 1545 ( 0.6) <0.001
p0 28616 ( 49.5) 117080 ( 42.1)
p1 765 ( 1.3) 3785 ( 1.4)
p1A 2157 ( 3.7) 7757 ( 2.8)
p1B 522 ( 0.9) 2050 ( 0.7)
p2 194 ( 0.3) 1150 ( 0.4)
p2A 697 ( 1.2) 2511 ( 0.9)
p2B 373 ( 0.6) 1547 ( 0.6)
p2C 411 ( 0.7) 1545 ( 0.6)
p3 1013 ( 1.8) 4283 ( 1.5)
pX 14289 ( 24.7) 115799 ( 41.6)
NA 8821 ( 15.2) 19159 ( 6.9)
TNM_PATH_M (%) N_A 4 ( 0.0) 1546 ( 0.6) <0.001
p1 234 ( 0.4) 2673 ( 1.0)
p1A 278 ( 0.5) 1488 ( 0.5)
p1B 267 ( 0.5) 1103 ( 0.4)
p1C 756 ( 1.3) 2799 ( 1.0)
pX 0 ( 0.0) 139768 ( 50.2)
NA 56323 ( 97.3) 128834 ( 46.3)
TNM_PATH_STAGE_GROUP (%) 0 533 ( 0.9) 1952 ( 0.7) <0.001
1 1169 ( 2.0) 5820 ( 2.1)
1A 13915 ( 24.0) 61591 ( 22.1)
1B 11287 ( 19.5) 51180 ( 18.4)
2 199 ( 0.3) 1313 ( 0.5)
2A 3925 ( 6.8) 18647 ( 6.7)
2B 2861 ( 4.9) 13085 ( 4.7)
2C 1501 ( 2.6) 6636 ( 2.4)
3 753 ( 1.3) 5995 ( 2.2)
3A 1850 ( 3.2) 7526 ( 2.7)
3B 1697 ( 2.9) 7347 ( 2.6)
3C 1346 ( 2.3) 5452 ( 2.0)
4 1845 ( 3.2) 9302 ( 3.3)
N_A 4 ( 0.0) 1545 ( 0.6)
99 11871 ( 20.5) 73084 ( 26.3)
NA 3106 ( 5.4) 7736 ( 2.8)
DX_RX_STARTED_DAYS (mean (sd)) 12.90 (27.62) 11.19 (29.78) <0.001
DX_SURG_STARTED_DAYS (mean (sd)) 12.21 (26.69) 10.62 (28.92) <0.001
DX_DEFSURG_STARTED_DAYS (mean (sd)) 34.09 (33.12) 31.01 (36.44) <0.001
MARGINS (%) No Residual 51064 ( 88.3) 245712 ( 88.3) <0.001
Residual, NOS 866 ( 1.5) 4434 ( 1.6)
Microscopic Resid 1152 ( 2.0) 4515 ( 1.6)
Macroscopic Resid 57 ( 0.1) 316 ( 0.1)
Not evaluable 168 ( 0.3) 839 ( 0.3)
No surg 3970 ( 6.9) 18608 ( 6.7)
Unknown 585 ( 1.0) 3787 ( 1.4)
MARGINS_YN (%) No 51064 ( 88.3) 245712 ( 88.3) 0.003
Yes 2075 ( 3.6) 9265 ( 3.3)
No surg/Unk/NA 4723 ( 8.2) 23234 ( 8.4)
SURG_DISCHARGE_DAYS (mean (sd)) 0.78 (5.84) 2.02 (9.89) <0.001
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 56685 ( 98.0) 264998 ( 95.3) <0.001
Unplan_Readmit_Same 304 ( 0.5) 2988 ( 1.1)
Plan_Readmit_Same 572 ( 1.0) 4583 ( 1.6)
PlanUnplan_Same 68 ( 0.1) 419 ( 0.2)
9 233 ( 0.4) 5223 ( 1.9)
RX_SUMM_RADIATION_F (%) None 55406 ( 95.8) 264311 ( 95.0) <0.001
Beam Radiation 2316 ( 4.0) 11767 ( 4.2)
Radioactive Implants 6 ( 0.0) 47 ( 0.0)
Radioisotopes 1 ( 0.0) 9 ( 0.0)
Beam + Imp or Isotopes 0 ( 0.0) 9 ( 0.0)
Radiation, NOS 18 ( 0.0) 119 ( 0.0)
Unknown 115 ( 0.2) 1949 ( 0.7)
PUF_30_DAY_MORT_CD_F (%) Alive_30 52152 ( 90.1) 253347 ( 91.1) <0.001
Dead_30 117 ( 0.2) 684 ( 0.2)
Unknown 1574 ( 2.7) 5350 ( 1.9)
NA 4019 ( 6.9) 18830 ( 6.8)
PUF_90_DAY_MORT_CD_F (%) Alive_90 50589 ( 87.4) 248035 ( 89.2) <0.001
Dead_90 394 ( 0.7) 2345 ( 0.8)
Unknown 2860 ( 4.9) 9001 ( 3.2)
NA 4019 ( 6.9) 18830 ( 6.8)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 31.33 (21.63) 58.15 (42.18) <0.001
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 40047 ( 69.2) 93220 ( 33.5) <0.001
Pos_LumphVasc_Inv 2138 ( 3.7) 5456 ( 2.0)
N_A 12 ( 0.0) 39 ( 0.0)
Unknown 15665 ( 27.1) 34140 ( 12.3)
NA 0 ( 0.0) 145356 ( 52.2)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 6404 ( 11.1) 15420 ( 5.5) <0.001
Robot_Assist 53 ( 0.1) 85 ( 0.0)
Robot_to_Open 8 ( 0.0) 25 ( 0.0)
Endo_Lap 93 ( 0.2) 320 ( 0.1)
Endo_Lap_to_Open 28 ( 0.0) 140 ( 0.1)
Open_Unknown 51264 ( 88.6) 116844 ( 42.0)
Unknown 12 ( 0.0) 21 ( 0.0)
NA 0 ( 0.0) 145356 ( 52.2)
SURG_RAD_SEQ (%) Surg Alone 52746 ( 91.2) 251930 ( 90.6) <0.001
Surg then Rad 1385 ( 2.4) 7626 ( 2.7)
Rad Alone 900 ( 1.6) 4034 ( 1.4)
No Treatment 2574 ( 4.4) 11690 ( 4.2)
Other 209 ( 0.4) 2675 ( 1.0)
Rad before and after Surg 6 ( 0.0) 32 ( 0.0)
Rad then Surg 42 ( 0.1) 224 ( 0.1)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 51057 ( 88.2) 241895 ( 86.9) <0.001
No Surg, No Rad, Yes Chemo 460 ( 0.8) 2306 ( 0.8)
No Surg, No Rad, No Chemo 2036 ( 3.5) 8957 ( 3.2)
Surg, No rad, Yes Chemo 478 ( 0.8) 3333 ( 1.2)
Other 1552 ( 2.7) 10121 ( 3.6)
Rad, No Surg, Yes Chemo 272 ( 0.5) 1432 ( 0.5)
Surg then Rad, No Chemo 1113 ( 1.9) 5921 ( 2.1)
Surg then Rad, Yes Chemo 240 ( 0.4) 1488 ( 0.5)
Rad, No Surg, No Chemo 606 ( 1.0) 2507 ( 0.9)
Rad then Surg, No Chemo 28 ( 0.0) 134 ( 0.0)
Rad then Surg, Yes Chemo 14 ( 0.0) 85 ( 0.0)
Rad before and after Surg, No Chemo 5 ( 0.0) 22 ( 0.0)
Rad before and after Surg, Yes Chemo 1 ( 0.0) 10 ( 0.0)
SURGERY_YN (%) No 3935 ( 6.8) 18037 ( 6.5) <0.001
Ukn 76 ( 0.1) 718 ( 0.3)
Yes 53851 ( 93.1) 259456 ( 93.3)
RADIATION_YN (%) No 55371 ( 95.7) 264124 ( 94.9) <0.001
Yes 2341 ( 4.0) 11951 ( 4.3)
NA 150 ( 0.3) 2136 ( 0.8)
CHEMO_YN (%) No 54956 ( 95.0) 260473 ( 93.6) <0.001
Yes 1475 ( 2.5) 8777 ( 3.2)
Ukn 1431 ( 2.5) 8961 ( 3.2)
mets_at_dx (%) Bone 236 ( 0.4) 647 ( 0.2) <0.001
Brain 425 ( 0.7) 1109 ( 0.4)
Liver 173 ( 0.3) 455 ( 0.2)
Lung 1296 ( 2.2) 3085 ( 1.1)
None/Other/Unk/NA 55732 ( 96.3) 272915 ( 98.1)
MEDICAID_EXPN_CODE (%) Non-Expansion State 0 ( 0.0) 121024 ( 43.5) <0.001
Jan 2014 Expansion States 21534 ( 37.2) 83126 ( 29.9)
Early Expansion States (2010-13) 36328 ( 62.8) 27157 ( 9.8)
Late Expansion States (> Jan 2014) 0 ( 0.0) 46904 ( 16.9)
SITE_TEXT (%) C44.0 Skin of lip, NOS 95 ( 0.2) 510 ( 0.2) 0.001
C44.1 Eyelid 181 ( 0.3) 975 ( 0.4)
C44.2 External ear 1682 ( 2.9) 8426 ( 3.0)
C44.3 Skin of ear and unspecified parts of face 5412 ( 9.4) 27343 ( 9.8)
C44.4 Skin of scalp and neck 4881 ( 8.4) 23580 ( 8.5)
C44.5 Skin of trunk 17330 ( 30.0) 82506 ( 29.7)
C44.6 Skin of upper limb and shoulder 14455 ( 25.0) 68861 ( 24.8)
C44.7 Skin of lower limb and hip 10302 ( 17.8) 48320 ( 17.4)
C44.8 Overlapping lesion of skin 64 ( 0.1) 318 ( 0.1)
C44.9 Skin, NOS 3460 ( 6.0) 17372 ( 6.2)

p_table(data,
        vars = c("YEAR_OF_DIAGNOSIS"),
        strata = c("MEDICAID_EXPN_CODE"))
level Non-Expansion State Jan 2014 Expansion States Early Expansion States (2010-13) Late Expansion States (> Jan 2014) Suppressed for Ages 0 - 39 p test
n 114831 100097 61288 44857 41837
YEAR_OF_DIAGNOSIS (%) 2004 7215 ( 6.3) 6291 ( 6.3) 3790 ( 6.2) 2684 ( 6.0) 3361 (8.0) NaN
2005 7840 ( 6.8) 6855 ( 6.8) 4054 ( 6.6) 2982 ( 6.6) 3669 (8.8)
2006 7954 ( 6.9) 7319 ( 7.3) 4202 ( 6.9) 2954 ( 6.6) 3550 (8.5)
2007 8545 ( 7.4) 7439 ( 7.4) 4550 ( 7.4) 3082 ( 6.9) 3585 (8.6)
2008 8944 ( 7.8) 7931 ( 7.9) 4698 ( 7.7) 3287 ( 7.3) 3493 (8.3)
2009 9456 ( 8.2) 8000 ( 8.0) 4906 ( 8.0) 3682 ( 8.2) 3582 (8.6)
2010 9553 ( 8.3) 8261 ( 8.3) 5015 ( 8.2) 3772 ( 8.4) 3367 (8.0)
2011 9941 ( 8.7) 8794 ( 8.8) 5369 ( 8.8) 3948 ( 8.8) 3293 (7.9)
2012 10449 ( 9.1) 8965 ( 9.0) 5489 ( 9.0) 4097 ( 9.1) 3394 (8.1)
2013 11143 ( 9.7) 9566 ( 9.6) 6101 (10.0) 4390 ( 9.8) 3425 (8.2)
2014 11455 (10.0) 10065 (10.1) 6319 (10.3) 4823 (10.8) 3507 (8.4)
2015 12336 (10.7) 10611 (10.6) 6795 (11.1) 5156 (11.5) 3611 (8.6)
2016 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 (0.0)
preExpMedicare  <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion" & INSURANCE_F == "Medicare"))
postExpMedicare <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion" & INSURANCE_F == "Medicare"))
# p = 0.25 when comparing change in proportion of patients with Medicare before and after ACA expansion
prop.test(c(preExpMedicare, postExpMedicare), 
          c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))

    2-sample test for equality of proportions with continuity correction

data:  c(preExpMedicare, postExpMedicare) out of c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% c(preExpMedicare, postExpMedicare) out of     filter(EXPN_GROUP == "Post-Expansion")))
X-squared = 10.134, df = 1, p-value = 0.001455
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.011941128 -0.002829993
sample estimates:
   prop 1    prop 2 
0.4465812 0.4539667 
preExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion") %>% 
                            filter(INSURANCE_F == "None"))
postExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion") %>% 
                             filter(INSURANCE_F == "None"))
# Significant decrease in the overall proportion of patients without insurance after ACA expansion 
prop.test(c(preExpNoInsurance, postExpNoInsurance), 
          c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))

    2-sample test for equality of proportions with continuity correction

data:  c(preExpNoInsurance, postExpNoInsurance) out of c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% c(preExpNoInsurance, postExpNoInsurance) out of     filter(EXPN_GROUP == "Post-Expansion")))
X-squared = 262.61, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
 0.009865323 0.012057405
sample estimates:
    prop 1     prop 2 
0.02340667 0.01244531 
p_table(no_Excludes, strata = "EXPN_GROUP", vars = "DX_RX_STARTED_DAYS")

level Post-Expansion Pre-Expansion p test
n 57862 278211
DX_RX_STARTED_DAYS (mean (sd)) 12.90 (27.62) 11.19 (29.78) <0.001

data <- data %>% mutate(Insured = INSURANCE_F != "Unknown")

Kaplan Meier Analysis

All

uni_var(test_var = "All", data_imp = data)
_________________________________________________
   
## All
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ All, data = data)

      n  events  median 0.95LCL 0.95UCL 
 362910   89972     164     162     165 

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ All, data = data)

 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 304157   21947    0.936 0.000417        0.935        0.937
   24 261059   17623    0.880 0.000569        0.879        0.881
   36 216079   13651    0.831 0.000675        0.829        0.832
   48 178134    9949    0.790 0.000757        0.788        0.791
   60 144302    7410    0.754 0.000828        0.752        0.756
  120  34572   17189    0.613 0.001249        0.610        0.615



   
## Univariable Cox Proportional Hazard Model for:  All

[1] "Only one level, no Cox model performed"




   
## Unadjusted Kaplan Meier Overall Survival Curve for:  All

Facility Type

uni_var(test_var = "FACILITY_TYPE_F", data_imp = data)
_________________________________________________
   
## FACILITY_TYPE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F, data = data)

   41837 observations deleted due to missingness 
                                                   n events median 0.95LCL 0.95UCL
FACILITY_TYPE_F=Community Cancer Program       20025   6627    126     122     132
FACILITY_TYPE_F=Comprehensive Comm Ca Program 114916  34156    144     142     146
FACILITY_TYPE_F=Academic/Research Program     147602  35238    162     158      NA
FACILITY_TYPE_F=Integrated Network Ca Program  38530  10808    147     143     152

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F, data = data)

41837 observations deleted due to missingness 
                FACILITY_TYPE_F=Community Cancer Program 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  16242    1969    0.897 0.00221        0.892        0.901
   24  13658    1293    0.823 0.00283        0.817        0.828
   36  11147     946    0.762 0.00323        0.756        0.768
   48   9167     668    0.713 0.00354        0.707        0.720
   60   7352     502    0.671 0.00380        0.664        0.679
  120   1706    1106    0.515 0.00535        0.504        0.525

                FACILITY_TYPE_F=Comprehensive Comm Ca Program 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  95753    8748    0.920 0.000818        0.919        0.922
   24  81903    6518    0.855 0.001087        0.853        0.857
   36  67972    4953    0.800 0.001267        0.798        0.803
   48  56271    3728    0.754 0.001406        0.751        0.756
   60  46023    2747    0.714 0.001522        0.711        0.717
  120  11044    6556    0.558 0.002195        0.553        0.562

                FACILITY_TYPE_F=Academic/Research Program 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 123953    7681    0.945 0.000613        0.944        0.946
   24 105844    7140    0.888 0.000870        0.886        0.890
   36  86436    5624    0.837 0.001051        0.835        0.839
   48  70096    4059    0.795 0.001191        0.793        0.797
   60  55804    3049    0.757 0.001317        0.755        0.760
  120  12528    6853    0.606 0.002069        0.602        0.610

                FACILITY_TYPE_F=Integrated Network Ca Program 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  32077    2792    0.924 0.00139        0.921        0.927
   24  27452    2016    0.863 0.00184        0.860        0.867
   36  22620    1625    0.809 0.00216        0.805        0.813
   48  18519    1144    0.765 0.00240        0.760        0.770
   60  14948     840    0.727 0.00261        0.722        0.733
  120   3461    2119    0.567 0.00391        0.559        0.575




   
## Univariable Cox Proportional Hazard Model for:  FACILITY_TYPE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F, data = data)

  n= 321073, number of events= 86829 
   (41837 observations deleted due to missingness)

                                                 coef exp(coef) se(coef)      z Pr(>|z|)    
FACILITY_TYPE_FComprehensive Comm Ca Program -0.16443   0.84837  0.01342 -12.25   <2e-16 ***
FACILITY_TYPE_FAcademic/Research Program     -0.35814   0.69898  0.01339 -26.75   <2e-16 ***
FACILITY_TYPE_FIntegrated Network Ca Program -0.20659   0.81336  0.01560 -13.24   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                             exp(coef) exp(-coef) lower .95 upper .95
FACILITY_TYPE_FComprehensive Comm Ca Program    0.8484      1.179    0.8263    0.8710
FACILITY_TYPE_FAcademic/Research Program        0.6990      1.431    0.6809    0.7176
FACILITY_TYPE_FIntegrated Network Ca Program    0.8134      1.229    0.7889    0.8386

Concordance= 0.535  (se = 0.001 )
Rsquare= 0.003   (max possible= 0.999 )
Likelihood ratio test= 1066  on 3 df,   p=0
Wald test            = 1079  on 3 df,   p=0
Score (logrank) test = 1085  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_TYPE_F

Facility Location

uni_var(test_var = "FACILITY_LOCATION_F", data_imp = data)
_________________________________________________
   
## FACILITY_LOCATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_LOCATION_F, data = data)

   41837 observations deleted due to missingness 
                                           n events median 0.95LCL 0.95UCL
FACILITY_LOCATION_F=New England        21270   5637    147     144     156
FACILITY_LOCATION_F=Middle Atlantic    52367  13264    154     150     157
FACILITY_LOCATION_F=South Atlantic     72796  20410    144     142     148
FACILITY_LOCATION_F=East North Central 53630  14618    151     149     155
FACILITY_LOCATION_F=East South Central 21009   6492    136     131     140
FACILITY_LOCATION_F=West North Central 27272   6879    157     151      NA
FACILITY_LOCATION_F=West South Central 16025   4815    146     139     153
FACILITY_LOCATION_F=Mountain           17133   4508    153     148     161
FACILITY_LOCATION_F=Pacific            39571  10206     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_LOCATION_F, data = data)

41837 observations deleted due to missingness 
                FACILITY_LOCATION_F=New England 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  17981    1277    0.937 0.00171        0.934        0.940
   24  15509    1065    0.879 0.00235        0.875        0.884
   36  12846     877    0.826 0.00281        0.821        0.832
   48  10594     601    0.785 0.00313        0.779        0.791
   60   8434     492    0.745 0.00345        0.739        0.752
  120   1929    1149    0.581 0.00537        0.571        0.592

                FACILITY_LOCATION_F=Middle Atlantic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  44645    3196    0.936 0.00110        0.934        0.938
   24  37929    2675    0.877 0.00150        0.874        0.880
   36  30808    2040    0.827 0.00179        0.823        0.830
   48  24591    1549    0.781 0.00203        0.778        0.785
   60  19240    1080    0.744 0.00223        0.739        0.748
  120   3694    2426    0.586 0.00363        0.578        0.593

                FACILITY_LOCATION_F=South Atlantic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  60080    4784    0.931 0.00097        0.929        0.932
   24  51140    3921    0.867 0.00133        0.865        0.870
   36  42412    3076    0.812 0.00158        0.809        0.815
   48  34971    2301    0.765 0.00176        0.761        0.768
   60  28464    1687    0.725 0.00192        0.721        0.729
  120   6770    4105    0.564 0.00283        0.558        0.569

                FACILITY_LOCATION_F=East North Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  44891    3581    0.930 0.00113        0.928        0.932
   24  38367    2827    0.869 0.00153        0.866        0.872
   36  31453    2229    0.815 0.00181        0.811        0.819
   48  25729    1592    0.771 0.00203        0.767        0.775
   60  20748    1195    0.732 0.00222        0.728        0.736
  120   4963    2833    0.577 0.00328        0.571        0.584

                FACILITY_LOCATION_F=East South Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  17689    1576    0.922 0.00190        0.918        0.925
   24  14959    1312    0.851 0.00257        0.846        0.856
   36  12339     969    0.792 0.00300        0.786        0.798
   48  10142     722    0.743 0.00333        0.736        0.749
   60   8140     527    0.701 0.00361        0.694        0.708
  120   1873    1217    0.537 0.00528        0.526        0.547

                FACILITY_LOCATION_F=West North Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  22526    1684    0.934 0.00155        0.931        0.937
   24  19360    1304    0.878 0.00210        0.874        0.882
   36  15791    1092    0.825 0.00251        0.820        0.830
   48  12855     751    0.783 0.00282        0.777        0.788
   60  10288     580    0.744 0.00310        0.738        0.750
  120   2307    1297    0.594 0.00476        0.585        0.603

                FACILITY_LOCATION_F=West South Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  13328    1417    0.908 0.00234        0.903        0.912
   24  11177     962    0.839 0.00303        0.833        0.845
   36   9198     685    0.784 0.00348        0.778        0.791
   48   7559     501    0.739 0.00384        0.731        0.746
   60   6099     389    0.698 0.00415        0.690        0.706
  120   1195     781    0.552 0.00610        0.540        0.564

                FACILITY_LOCATION_F=Mountain 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  14210    1161    0.928 0.00202        0.925        0.932
   24  12205     861    0.870 0.00271        0.865        0.875
   36  10058     666    0.819 0.00318        0.813        0.826
   48   8267     494    0.776 0.00356        0.769        0.783
   60   6834     341    0.742 0.00386        0.734        0.749
  120   1644     849    0.593 0.00586        0.582        0.605

                FACILITY_LOCATION_F=Pacific 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  32675    2514    0.933 0.00129        0.931        0.936
   24  28211    2040    0.873 0.00177        0.869        0.876
   36  23270    1514    0.822 0.00209        0.818        0.827
   48  19345    1088    0.781 0.00233        0.777        0.786
   60  15880     847    0.744 0.00254        0.740        0.749
  120   4364    1977    0.606 0.00364        0.599        0.613




   
## Univariable Cox Proportional Hazard Model for:  FACILITY_LOCATION_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_LOCATION_F, data = data)

  n= 321073, number of events= 86829 
   (41837 observations deleted due to missingness)

                                            coef  exp(coef)   se(coef)      z Pr(>|z|)    
FACILITY_LOCATION_FMiddle Atlantic    -0.0018090  0.9981926  0.0159011 -0.114  0.90942    
FACILITY_LOCATION_FSouth Atlantic      0.0745691  1.0774198  0.0150466  4.956  7.2e-07 ***
FACILITY_LOCATION_FEast North Central  0.0430144  1.0439529  0.0156783  2.744  0.00608 ** 
FACILITY_LOCATION_FEast South Central  0.1684812  1.1835059  0.0182054  9.254  < 2e-16 ***
FACILITY_LOCATION_FWest North Central -0.0144924  0.9856121  0.0179659 -0.807  0.41986    
FACILITY_LOCATION_FWest South Central  0.1737202  1.1897227  0.0196242  8.852  < 2e-16 ***
FACILITY_LOCATION_FMountain           -0.0008789  0.9991215  0.0199809 -0.044  0.96492    
FACILITY_LOCATION_FPacific            -0.0363362  0.9643160  0.0165960 -2.189  0.02856 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                      exp(coef) exp(-coef) lower .95 upper .95
FACILITY_LOCATION_FMiddle Atlantic       0.9982     1.0018    0.9676    1.0298
FACILITY_LOCATION_FSouth Atlantic        1.0774     0.9281    1.0461    1.1097
FACILITY_LOCATION_FEast North Central    1.0440     0.9579    1.0124    1.0765
FACILITY_LOCATION_FEast South Central    1.1835     0.8449    1.1420    1.2265
FACILITY_LOCATION_FWest North Central    0.9856     1.0146    0.9515    1.0209
FACILITY_LOCATION_FWest South Central    1.1897     0.8405    1.1448    1.2364
FACILITY_LOCATION_FMountain              0.9991     1.0009    0.9608    1.0390
FACILITY_LOCATION_FPacific               0.9643     1.0370    0.9335    0.9962

Concordance= 0.517  (se = 0.001 )
Rsquare= 0.001   (max possible= 0.999 )
Likelihood ratio test= 331.4  on 8 df,   p=0
Wald test            = 337.3  on 8 df,   p=0
Score (logrank) test = 337.9  on 8 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_LOCATION_F

Facility Geography

uni_var(test_var = "FACILITY_GEOGRAPHY", data_imp = data)
_________________________________________________
   
## FACILITY_GEOGRAPHY
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_GEOGRAPHY, data = data)

   41837 observations deleted due to missingness 
                                  n events median 0.95LCL 0.95UCL
FACILITY_GEOGRAPHY=Northeast  73637  18901    153     147     156
FACILITY_GEOGRAPHY=South      88821  25225    144     142     148
FACILITY_GEOGRAPHY=Midwest   101911  27989    149     146     151
FACILITY_GEOGRAPHY=West       56704  14714     NA     161      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_GEOGRAPHY, data = data)

41837 observations deleted due to missingness 
                FACILITY_GEOGRAPHY=Northeast 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  62626    4473    0.936 0.000923        0.934        0.938
   24  53438    3740    0.878 0.001266        0.876        0.880
   36  43654    2917    0.827 0.001508        0.824        0.830
   48  35185    2150    0.783 0.001702        0.779        0.786
   60  27674    1572    0.744 0.001875        0.741        0.748
  120   5623    3575    0.584 0.003008        0.578        0.590

                FACILITY_GEOGRAPHY=South 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  73408    6201    0.926 0.000901        0.925        0.928
   24  62317    4883    0.862 0.001222        0.860        0.864
   36  51610    3761    0.807 0.001438        0.804        0.810
   48  42530    2802    0.760 0.001604        0.757        0.763
   60  34563    2076    0.720 0.001743        0.717        0.724
  120   7965    4886    0.561 0.002568        0.556        0.566

                FACILITY_GEOGRAPHY=Midwest 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  85106    6841    0.929 0.000825        0.928        0.931
   24  72686    5443    0.868 0.001118        0.865        0.870
   36  59583    4290    0.813 0.001323        0.810        0.815
   48  48726    3065    0.768 0.001478        0.765        0.771
   60  39176    2302    0.729 0.001615        0.725        0.732
  120   9143    5347    0.573 0.002409        0.568        0.578

                FACILITY_GEOGRAPHY=West 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  46885    3675    0.932 0.00109        0.930        0.934
   24  40416    2901    0.872 0.00148        0.869        0.875
   36  33328    2180    0.822 0.00175        0.818        0.825
   48  27612    1582    0.780 0.00195        0.776        0.784
   60  22714    1188    0.744 0.00212        0.740        0.748
  120   6008    2826    0.602 0.00309        0.596        0.608




   
## Univariable Cox Proportional Hazard Model for:  FACILITY_GEOGRAPHY

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_GEOGRAPHY, data = data)

  n= 321073, number of events= 86829 
   (41837 observations deleted due to missingness)

                               coef exp(coef)  se(coef)      z Pr(>|z|)    
FACILITY_GEOGRAPHYSouth    0.094031  1.098594  0.009622  9.772  < 2e-16 ***
FACILITY_GEOGRAPHYMidwest  0.057178  1.058844  0.009416  6.072 1.26e-09 ***
FACILITY_GEOGRAPHYWest    -0.024315  0.975978  0.010999 -2.211   0.0271 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                          exp(coef) exp(-coef) lower .95 upper .95
FACILITY_GEOGRAPHYSouth       1.099     0.9103    1.0781    1.1195
FACILITY_GEOGRAPHYMidwest     1.059     0.9444    1.0395    1.0786
FACILITY_GEOGRAPHYWest        0.976     1.0246    0.9552    0.9972

Concordance= 0.512  (se = 0.001 )
Rsquare= 0.001   (max possible= 0.999 )
Likelihood ratio test= 173.3  on 3 df,   p=0
Wald test            = 172.8  on 3 df,   p=0
Score (logrank) test = 172.9  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_GEOGRAPHY

Age Group

uni_var(test_var = "AGE_F", data_imp = data)
_________________________________________________
   
## AGE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_F, data = data)

   34 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
AGE_F=(0,54]   123853  13264     NA      NA      NA
AGE_F=(54,64]   79330  13732     NA      NA      NA
AGE_F=(64,74]   76938  20008  139.4   138.0   141.9
AGE_F=(74,100]  82755  42964   59.7    59.1    60.4

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_F, data = data)

34 observations deleted due to missingness 
                AGE_F=(0,54] 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 106374    3468    0.970 0.000498        0.969        0.971
   24  94556    2739    0.944 0.000690        0.943        0.946
   36  81723    2031    0.923 0.000823        0.921        0.924
   48  70239    1454    0.905 0.000927        0.903        0.907
   60  59008    1047    0.891 0.001015        0.889        0.893
  120  16859    2249    0.839 0.001483        0.836        0.842

                AGE_F=(54,64] 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  66772    3653    0.951 0.000786        0.950        0.953
   24  58185    2649    0.912 0.001064        0.910        0.914
   36  48718    2102    0.877 0.001271        0.874        0.879
   48  40530    1456    0.849 0.001429        0.846        0.851
   60  33238    1023    0.825 0.001564        0.822        0.828
  120   8217    2499    0.725 0.002443        0.721        0.730

                AGE_F=(64,74] 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  64339    4647    0.936 0.000904        0.935        0.938
   24  55056    3600    0.882 0.001228        0.879        0.884
   36  44903    2968    0.831 0.001472        0.828        0.834
   48  36459    2189    0.787 0.001666        0.784        0.790
   60  28999    1618    0.749 0.001837        0.745        0.752
  120   6181    4296    0.565 0.003012        0.559        0.571

                AGE_F=(74,100] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  66647   10178    0.872 0.00119        0.870        0.874
   24  53243    8634    0.755 0.00156        0.752        0.758
   36  40718    6549    0.656 0.00177        0.653        0.660
   48  30892    4850    0.572 0.00191        0.569        0.576
   60  23046    3721    0.498 0.00202        0.494        0.502
  120   3310    8145    0.229 0.00247        0.225        0.234




   
## Univariable Cox Proportional Hazard Model for:  AGE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_F, data = data)

  n= 362876, number of events= 89968 
   (34 observations deleted due to missingness)

                  coef exp(coef) se(coef)      z Pr(>|z|)    
AGE_F(54,64]  0.570757  1.769606 0.012178  46.87   <2e-16 ***
AGE_F(64,74]  1.044519  2.842031 0.011211  93.17   <2e-16 ***
AGE_F(74,100] 1.915597  6.790994 0.009999 191.59   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
AGE_F(54,64]      1.770     0.5651     1.728     1.812
AGE_F(64,74]      2.842     0.3519     2.780     2.905
AGE_F(74,100]     6.791     0.1473     6.659     6.925

Concordance= 0.68  (se = 0.001 )
Rsquare= 0.125   (max possible= 0.998 )
Likelihood ratio test= 48267  on 3 df,   p=0
Wald test            = 46114  on 3 df,   p=0
Score (logrank) test = 56187  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  AGE_F

Age Group

uni_var(test_var = "AGE_40", data_imp = data)
_________________________________________________
   
## AGE_40
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_40, data = data)

   34 observations deleted due to missingness 
                     n events median 0.95LCL 0.95UCL
AGE_40=(0,40]    45430   3484     NA      NA      NA
AGE_40=(40,100] 317446  86484    150     149     152

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_40, data = data)

34 observations deleted due to missingness 
                AGE_40=(0,40] 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  39270     846    0.980 0.000677        0.979        0.981
   24  34993     735    0.961 0.000965        0.959        0.963
   36  30341     544    0.945 0.001165        0.943        0.947
   48  26165     400    0.932 0.001325        0.929        0.934
   60  21941     292    0.921 0.001462        0.918        0.923
  120   6350     612    0.881 0.002175        0.877        0.885

                AGE_40=(40,100] 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 264862   21100    0.930 0.000465        0.929        0.931
   24 226047   16887    0.868 0.000632        0.867        0.870
   36 185721   13106    0.815 0.000748        0.813        0.816
   48 151955    9549    0.770 0.000836        0.768        0.771
   60 122350    7117    0.731 0.000914        0.729        0.732
  120  28217   16577    0.574 0.001372        0.572        0.577




   
## Univariable Cox Proportional Hazard Model for:  AGE_40

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_40, data = data)

  n= 362876, number of events= 89968 
   (34 observations deleted due to missingness)

                  coef exp(coef) se(coef)     z Pr(>|z|)    
AGE_40(40,100] 1.41156   4.10235  0.01729 81.66   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

               exp(coef) exp(-coef) lower .95 upper .95
AGE_40(40,100]     4.102     0.2438     3.966     4.244

Concordance= 0.548  (se = 0.001 )
Rsquare= 0.029   (max possible= 0.998 )
Likelihood ratio test= 10562  on 1 df,   p=0
Wald test            = 6669  on 1 df,   p=0
Score (logrank) test = 7851  on 1 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  AGE_40

Gender

uni_var(test_var = "SEX_F", data_imp = data)
_________________________________________________
   
## SEX_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SEX_F, data = data)

                  n events median 0.95LCL 0.95UCL
SEX_F=Male   207765  60900    140     139     142
SEX_F=Female 155145  29072     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SEX_F, data = data)

                SEX_F=Male 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 172446   14990    0.924 0.000597        0.923        0.925
   24 146109   12112    0.856 0.000810        0.855        0.858
   36 119424    9256    0.799 0.000954        0.797        0.800
   48  97169    6775    0.750 0.001063        0.748        0.752
   60  77841    5036    0.708 0.001158        0.706        0.710
  120  17787   11370    0.546 0.001703        0.542        0.549

                SEX_F=Female 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 131711    6957    0.953 0.000555        0.952        0.954
   24 114950    5511    0.911 0.000763        0.910        0.913
   36  96655    4395    0.874 0.000915        0.872        0.876
   48  80965    3174    0.843 0.001033        0.841        0.845
   60  66461    2374    0.816 0.001138        0.814        0.819
  120  16785    5819    0.703 0.001777        0.700        0.707




   
## Univariable Cox Proportional Hazard Model for:  SEX_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SEX_F, data = data)

  n= 362910, number of events= 89972 

                 coef exp(coef)  se(coef)      z Pr(>|z|)    
SEX_FFemale -0.527427  0.590122  0.007132 -73.95   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

            exp(coef) exp(-coef) lower .95 upper .95
SEX_FFemale    0.5901      1.695    0.5819    0.5984

Concordance= 0.561  (se = 0.001 )
Rsquare= 0.016   (max possible= 0.998 )
Likelihood ratio test= 5762  on 1 df,   p=0
Wald test            = 5469  on 1 df,   p=0
Score (logrank) test = 5597  on 1 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SEX_F

RACE_F

uni_var(test_var = "RACE_F", data_imp = data)
_________________________________________________
   
## RACE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RACE_F, data = data)

                      n events median 0.95LCL 0.95UCL
RACE_F=White     353647  87552  164.4   161.8     165
RACE_F=Black       2116    907   73.1    65.5      84
RACE_F=Other/Unk   6088   1213     NA      NA      NA
RACE_F=Asian       1059    300     NA   141.8      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RACE_F, data = data)

                RACE_F=White 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 296701   21227    0.937 0.000421        0.936        0.938
   24 254717   17131    0.880 0.000575        0.879        0.882
   36 210851   13290    0.831 0.000683        0.830        0.833
   48 173829    9704    0.790 0.000766        0.789        0.792
   60 140792    7222    0.755 0.000838        0.753        0.756
  120  33608   16816    0.613 0.001267        0.610        0.615

                RACE_F=Black 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1583     341    0.831 0.00835        0.815        0.848
   24   1291     170    0.739 0.00999        0.720        0.759
   36   1016     125    0.663 0.01105        0.641        0.685
   48    789      89    0.601 0.01182        0.578        0.624
   60    601      68    0.545 0.01251        0.521        0.570
  120    132     102    0.409 0.01570        0.379        0.441

                RACE_F=Other/Unk 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5045     278    0.951 0.00288        0.945        0.956
   24   4376     251    0.901 0.00409        0.893        0.909
   36   3680     188    0.860 0.00489        0.850        0.870
   48   3110     123    0.829 0.00545        0.819        0.840
   60   2594     103    0.800 0.00598        0.788        0.812
  120    749     245    0.689 0.00864        0.672        0.706

                RACE_F=Asian 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    828     101    0.898 0.00962        0.880        0.917
   24    675      71    0.817 0.01270        0.793        0.842
   36    532      48    0.754 0.01465        0.726        0.783
   48    406      33    0.702 0.01620        0.671        0.734
   60    315      17    0.670 0.01723        0.637        0.704
  120     83      26    0.585 0.02227        0.543        0.630




   
## Univariable Cox Proportional Hazard Model for:  RACE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RACE_F, data = data)

  n= 362910, number of events= 89972 

                    coef exp(coef) se(coef)      z Pr(>|z|)    
RACE_FBlack      0.75331   2.12402  0.03338 22.569  < 2e-16 ***
RACE_FOther/Unk -0.25173   0.77745  0.02891 -8.707  < 2e-16 ***
RACE_FAsian      0.28409   1.32855  0.05783  4.912 9.01e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
RACE_FBlack        2.1240     0.4708    1.9895    2.2676
RACE_FOther/Unk    0.7775     1.2862    0.7346    0.8228
RACE_FAsian        1.3285     0.7527    1.1862    1.4880

Concordance= 0.505  (se = 0 )
Rsquare= 0.001   (max possible= 0.998 )
Likelihood ratio test= 511.8  on 3 df,   p=0
Wald test            = 613.3  on 3 df,   p=0
Score (logrank) test = 639  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RACE_F

Hispanic

uni_var(test_var = "HISPANIC", data_imp = data)
_________________________________________________
   
## HISPANIC
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISPANIC, data = data)

                      n events median 0.95LCL 0.95UCL
HISPANIC=No      338276  83282    163     162     165
HISPANIC=Yes       5052   1341     NA     145      NA
HISPANIC=Unknown  19582   5349     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISPANIC, data = data)

                HISPANIC=No 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 283450   20334    0.937 0.000431        0.936        0.937
   24 242887   16388    0.880 0.000589        0.879        0.881
   36 200359   12724    0.831 0.000700        0.830        0.832
   48 164465    9264    0.790 0.000786        0.788        0.791
   60 132607    6841    0.754 0.000861        0.752        0.756
  120  31127   15746    0.612 0.001309        0.609        0.614

                HISPANIC=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4050     420    0.912 0.00413        0.903        0.920
   24   3338     303    0.840 0.00549        0.829        0.851
   36   2681     199    0.786 0.00634        0.773        0.798
   48   2162     134    0.743 0.00698        0.730        0.757
   60   1709      90    0.709 0.00753        0.695        0.724
  120    362     174    0.595 0.01064        0.575        0.616

                HISPANIC=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  16657    1193    0.936 0.00179        0.933        0.940
   24  14834     932    0.882 0.00241        0.878        0.887
   36  13039     728    0.837 0.00280        0.832        0.843
   48  11507     551    0.800 0.00309        0.794        0.806
   60   9986     479    0.765 0.00334        0.759        0.772
  120   3083    1269    0.629 0.00458        0.620        0.638




   
## Univariable Cox Proportional Hazard Model for:  HISPANIC

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISPANIC, data = data)

  n= 362910, number of events= 89972 

                    coef exp(coef) se(coef)      z Pr(>|z|)    
HISPANICYes      0.17257   1.18835  0.02753  6.269 3.64e-10 ***
HISPANICUnknown -0.05147   0.94983  0.01412 -3.646 0.000266 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
HISPANICYes        1.1883     0.8415    1.1259    1.2542
HISPANICUnknown    0.9498     1.0528    0.9239    0.9765

Concordance= 0.503  (se = 0 )
Rsquare= 0   (max possible= 0.998 )
Likelihood ratio test= 52.05  on 2 df,   p=4.972e-12
Wald test            = 54.04  on 2 df,   p=1.84e-12
Score (logrank) test = 54.15  on 2 df,   p=1.741e-12
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  HISPANIC

Insurance Status

uni_var(test_var = "INSURANCE_F", data_imp = data)
_________________________________________________
   
## INSURANCE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ INSURANCE_F, data = data)

                                  n events median 0.95LCL 0.95UCL
INSURANCE_F=Private          196431  26154     NA      NA      NA
INSURANCE_F=None               8903   2462  165.2     165      NA
INSURANCE_F=Medicaid           9359   2921  139.3     127   150.7
INSURANCE_F=Medicare         144437  57598   86.8      86    87.6
INSURANCE_F=Other Government   3780    837  160.3     160      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ INSURANCE_F, data = data)

                INSURANCE_F=Private 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 168550    5841    0.968 0.000407        0.968        0.969
   24 149287    5097    0.938 0.000577        0.937        0.939
   36 127672    4149    0.910 0.000703        0.909        0.912
   48 108390    2942    0.888 0.000798        0.886        0.889
   60  90243    2240    0.868 0.000883        0.866        0.870
  120  24426    5180    0.790 0.001370        0.787        0.792

                INSURANCE_F=None 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7020     885    0.895 0.00335        0.888        0.901
   24   5936     498    0.829 0.00422        0.820        0.837
   36   4958     357    0.776 0.00478        0.767        0.786
   48   4101     250    0.735 0.00520        0.724        0.745
   60   3264     141    0.707 0.00550        0.696        0.718
  120    777     302    0.604 0.00757        0.589        0.619

                INSURANCE_F=Medicaid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7089    1207    0.863 0.00366        0.856        0.871
   24   5580     624    0.783 0.00452        0.774        0.792
   36   4214     372    0.726 0.00508        0.716        0.736
   48   3298     240    0.680 0.00555        0.669        0.691
   60   2551     145    0.647 0.00592        0.636        0.659
  120    552     301    0.528 0.00837        0.512        0.544

                INSURANCE_F=Medicare 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 118458   13738    0.901 0.000806        0.899        0.902
   24  97750   11209    0.812 0.001077        0.810        0.814
   36  77222    8653    0.735 0.001253        0.733        0.738
   48  60748    6438    0.669 0.001385        0.667        0.672
   60  46997    4839    0.611 0.001496        0.608        0.614
  120   8575   11291    0.379 0.002129        0.375        0.383

                INSURANCE_F=Other Government 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3040     276    0.922 0.00453        0.913        0.931
   24   2506     195    0.859 0.00605        0.847        0.871
   36   2013     120    0.815 0.00698        0.801        0.828
   48   1597      79    0.780 0.00771        0.765        0.795
   60   1247      45    0.755 0.00828        0.739        0.772
  120    242     115    0.637 0.01336        0.611        0.663




   
## Univariable Cox Proportional Hazard Model for:  INSURANCE_F
X matrix deemed to be singular; variable 5
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ INSURANCE_F, data = data)

  n= 362910, number of events= 89972 

                                coef exp(coef) se(coef)      z Pr(>|z|)    
INSURANCE_FNone             0.897310  2.452997 0.021084  42.56   <2e-16 ***
INSURANCE_FMedicaid         1.179156  3.251627 0.019523  60.40   <2e-16 ***
INSURANCE_FMedicare         1.313677  3.719828 0.007489 175.40   <2e-16 ***
INSURANCE_FOther Government 0.727012  2.068889 0.035119  20.70   <2e-16 ***
INSURANCE_FUnknown                NA        NA 0.000000     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                            exp(coef) exp(-coef) lower .95 upper .95
INSURANCE_FNone                 2.453     0.4077     2.354     2.556
INSURANCE_FMedicaid             3.252     0.3075     3.130     3.378
INSURANCE_FMedicare             3.720     0.2688     3.666     3.775
INSURANCE_FOther Government     2.069     0.4834     1.931     2.216
INSURANCE_FUnknown                 NA         NA        NA        NA

Concordance= 0.647  (se = 0.001 )
Rsquare= 0.091   (max possible= 0.998 )
Likelihood ratio test= 34566  on 4 df,   p=0
Wald test            = 31004  on 4 df,   p=0
Score (logrank) test = 35429  on 4 df,   p=0
Removed 2 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  INSURANCE_F

Overall Survival pre/post-ACA expansion

uni_var(test_var = "EXPN_GROUP", data_imp = no_Excludes)
_________________________________________________
   
## EXPN_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EXPN_GROUP, data = no_Excludes)

                               n events median 0.95LCL 0.95UCL
EXPN_GROUP=Post-Expansion  57862  10046     NA      NA      NA
EXPN_GROUP=Pre-Expansion  278211  84641    147     146     149

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EXPN_GROUP, data = no_Excludes)

                EXPN_GROUP=Post-Expansion 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  45462    3817    0.929 0.00111        0.927        0.931
   24  33837    2728    0.868 0.00154        0.865        0.871
   36  20120    1681    0.815 0.00192        0.811        0.818
   48  11866     896    0.769 0.00236        0.764        0.773
   60   7412     497    0.729 0.00283        0.724        0.735

                EXPN_GROUP=Pre-Expansion 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 233209   20815    0.922 0.000522        0.921        0.923
   24 202961   16126    0.856 0.000695        0.855        0.857
   36 174188   12427    0.801 0.000806        0.800        0.803
   48 147051    9219    0.757 0.000885        0.755        0.758
   60 120495    6991    0.718 0.000953        0.716        0.720
  120  29573   16837    0.566 0.001353        0.563        0.568




   
## Univariable Cox Proportional Hazard Model for:  EXPN_GROUP

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EXPN_GROUP, data = no_Excludes)

  n= 336073, number of events= 94687 

                           coef exp(coef) se(coef)     z Pr(>|z|)    
EXPN_GROUPPre-Expansion 0.06250   1.06449  0.01072 5.828 5.61e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
EXPN_GROUPPre-Expansion     1.064     0.9394     1.042     1.087

Concordance= 0.504  (se = 0.001 )
Rsquare= 0   (max possible= 0.999 )
Likelihood ratio test= 34.49  on 1 df,   p=4.286e-09
Wald test            = 33.97  on 1 df,   p=5.607e-09
Score (logrank) test = 33.98  on 1 df,   p=5.576e-09





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  EXPN_GROUP

Education

uni_var(test_var = "EDUCATION_F", data_imp = data)
_________________________________________________
   
## EDUCATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EDUCATION_F, data = data)

   1490 observations deleted due to missingness 
                              n events median 0.95LCL 0.95UCL
EDUCATION_F=21% or more   36157  11317    139     134     142
EDUCATION_F=13 - 20.9%    77301  21594    151     147     154
EDUCATION_F=7 - 12.9%    126598  31601    162     161      NA
EDUCATION_F=Less than 7% 121364  25137     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EDUCATION_F, data = data)

1490 observations deleted due to missingness 
                EDUCATION_F=21% or more 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  29746    3036    0.912 0.00153        0.909        0.915
   24  25060    2349    0.837 0.00204        0.833        0.841
   36  20505    1695    0.777 0.00236        0.772        0.782
   48  16928    1209    0.728 0.00260        0.723        0.733
   60  13674     884    0.687 0.00279        0.682        0.693
  120   3213    1892    0.540 0.00392        0.533        0.548

                EDUCATION_F=13 - 20.9% 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  64315    5467    0.926 0.000969        0.924        0.927
   24  54669    4263    0.862 0.001306        0.859        0.864
   36  44993    3213    0.808 0.001533        0.805        0.811
   48  36957    2354    0.763 0.001708        0.759        0.766
   60  29767    1743    0.724 0.001859        0.720        0.727
  120   7011    4059    0.570 0.002748        0.564        0.575

                EDUCATION_F=7 - 12.9% 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 106148    7706    0.936 0.000708        0.934        0.937
   24  90972    6119    0.880 0.000964        0.878        0.881
   36  74962    4856    0.829 0.001148        0.827        0.832
   48  61570    3532    0.787 0.001289        0.785        0.790
   60  49939    2620    0.751 0.001412        0.748        0.754
  120  12025    5994    0.609 0.002122        0.605        0.613

                EDUCATION_F=Less than 7% 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 102819    5667    0.951 0.000640        0.949        0.952
   24  89468    4825    0.904 0.000893        0.902        0.906
   36  74880    3837    0.863 0.001073        0.861        0.865
   48  62086    2815    0.828 0.001215        0.826        0.830
   60  50461    2131    0.797 0.001342        0.794        0.800
  120  12233    5190    0.667 0.002110        0.663        0.671




   
## Univariable Cox Proportional Hazard Model for:  EDUCATION_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EDUCATION_F, data = data)

  n= 361420, number of events= 89649 
   (1490 observations deleted due to missingness)

                            coef exp(coef) se(coef)      z Pr(>|z|)    
EDUCATION_F13 - 20.9%   -0.13004   0.87806  0.01160 -11.21   <2e-16 ***
EDUCATION_F7 - 12.9%    -0.26039   0.77075  0.01095 -23.77   <2e-16 ***
EDUCATION_FLess than 7% -0.47909   0.61935  0.01132 -42.32   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
EDUCATION_F13 - 20.9%      0.8781      1.139    0.8583    0.8983
EDUCATION_F7 - 12.9%       0.7707      1.297    0.7544    0.7875
EDUCATION_FLess than 7%    0.6193      1.615    0.6058    0.6332

Concordance= 0.548  (se = 0.001 )
Rsquare= 0.006   (max possible= 0.998 )
Likelihood ratio test= 2342  on 3 df,   p=0
Wald test            = 2335  on 3 df,   p=0
Score (logrank) test = 2360  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  EDUCATION_F

Urban/Rural

uni_var(test_var = "U_R_F", data_imp = data)
_________________________________________________
   
## U_R_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ U_R_F, data = data)

   11037 observations deleted due to missingness 
                 n events median 0.95LCL 0.95UCL
U_R_F=Metro 297888  72541    165     164      NA
U_R_F=Urban  47786  12979    153     148     157
U_R_F=Rural   6199   1825    141     134     149

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ U_R_F, data = data)

11037 observations deleted due to missingness 
                U_R_F=Metro 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 249758   17698    0.937 0.000456        0.936        0.938
   24 214853   14140    0.882 0.000623        0.881        0.883
   36 178176   10974    0.834 0.000739        0.833        0.835
   48 147112    8058    0.794 0.000830        0.792        0.795
   60 119251    6004    0.759 0.000909        0.757        0.760
  120  28714   13883    0.620 0.001369        0.617        0.623

                U_R_F=Urban 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  40132    3196    0.930 0.00120        0.927        0.932
   24  34165    2546    0.868 0.00163        0.865        0.871
   36  28024    1986    0.814 0.00192        0.811        0.818
   48  22962    1412    0.770 0.00215        0.766        0.775
   60  18526    1058    0.732 0.00234        0.727        0.737
  120   4346    2477    0.576 0.00353        0.569        0.583

                U_R_F=Rural 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5179     439    0.926 0.00341        0.919        0.932
   24   4360     378    0.855 0.00470        0.846        0.865
   36   3585     277    0.798 0.00551        0.787        0.809
   48   2881     216    0.746 0.00619        0.734        0.758
   60   2314     141    0.706 0.00670        0.693        0.719
  120    516     332    0.549 0.00986        0.530        0.568




   
## Univariable Cox Proportional Hazard Model for:  U_R_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ U_R_F, data = data)

  n= 351873, number of events= 87345 
   (11037 observations deleted due to missingness)

               coef exp(coef) se(coef)     z Pr(>|z|)    
U_R_FUrban 0.128419  1.137029 0.009531 13.47   <2e-16 ***
U_R_FRural 0.227046  1.254888 0.023701  9.58   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

           exp(coef) exp(-coef) lower .95 upper .95
U_R_FUrban     1.137     0.8795     1.116     1.158
U_R_FRural     1.255     0.7969     1.198     1.315

Concordance= 0.509  (se = 0.001 )
Rsquare= 0.001   (max possible= 0.998 )
Likelihood ratio test= 249  on 2 df,   p=0
Wald test            = 258.5  on 2 df,   p=0
Score (logrank) test = 259.1  on 2 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  U_R_F

Class (treatment at performing facility)

uni_var(test_var = "CLASS_OF_CASE_F", data_imp = data)
_________________________________________________
   
## CLASS_OF_CASE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CLASS_OF_CASE_F, data = data)

                                    n events median 0.95LCL 0.95UCL
CLASS_OF_CASE_F=Other_Facility   3475   1686   23.9    21.4    26.5
CLASS_OF_CASE_F=All_Part_Prim  359435  88286  164.5   162.0      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CLASS_OF_CASE_F, data = data)

                CLASS_OF_CASE_F=Other_Facility 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1572    1057    0.634 0.00910        0.616        0.652
   24   1031     311    0.499 0.00989        0.480        0.518
   36    720     143    0.422 0.01025        0.402        0.443
   48    513      63    0.380 0.01052        0.360        0.401
   60    390      35    0.351 0.01078        0.331        0.373
  120     61      73    0.245 0.01402        0.219        0.274

                CLASS_OF_CASE_F=All_Part_Prim 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 302585   20890    0.939 0.000411        0.938        0.940
   24 260028   17312    0.883 0.000565        0.882        0.884
   36 215359   13508    0.834 0.000673        0.833        0.835
   48 177621    9886    0.793 0.000756        0.791        0.794
   60 143912    7375    0.757 0.000829        0.756        0.759
  120  34511   17116    0.616 0.001253        0.613        0.618




   
## Univariable Cox Proportional Hazard Model for:  CLASS_OF_CASE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CLASS_OF_CASE_F, data = data)

  n= 362910, number of events= 89972 

                                 coef exp(coef) se(coef)      z Pr(>|z|)    
CLASS_OF_CASE_FAll_Part_Prim -1.53139   0.21624  0.02463 -62.18   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                             exp(coef) exp(-coef) lower .95 upper .95
CLASS_OF_CASE_FAll_Part_Prim    0.2162      4.625     0.206    0.2269

Concordance= 0.51  (se = 0 )
Rsquare= 0.007   (max possible= 0.998 )
Likelihood ratio test= 2498  on 1 df,   p=0
Wald test            = 3867  on 1 df,   p=0
Score (logrank) test = 4684  on 1 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  CLASS_OF_CASE_F

Year

uni_var(test_var = "YEAR_OF_DIAGNOSIS", data_imp = data)
_________________________________________________
   
## YEAR_OF_DIAGNOSIS
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ YEAR_OF_DIAGNOSIS, data = data)

                           n events median 0.95LCL 0.95UCL
YEAR_OF_DIAGNOSIS=2004 23341   8432     NA   164.6      NA
YEAR_OF_DIAGNOSIS=2005 25400   8688  157.6   154.8      NA
YEAR_OF_DIAGNOSIS=2006 25979   8817  143.3   143.3      NA
YEAR_OF_DIAGNOSIS=2007 27201   8757     NA   131.0      NA
YEAR_OF_DIAGNOSIS=2008 28353   8878     NA      NA      NA
YEAR_OF_DIAGNOSIS=2009 29626   8524     NA   107.1      NA
YEAR_OF_DIAGNOSIS=2010 29968   8076   95.5    95.0      NA
YEAR_OF_DIAGNOSIS=2011 31345   7787     NA    83.3      NA
YEAR_OF_DIAGNOSIS=2012 32394   6814   71.8    71.7      NA
YEAR_OF_DIAGNOSIS=2013 34625   6286     NA      NA      NA
YEAR_OF_DIAGNOSIS=2014 36169   5073     NA      NA      NA
YEAR_OF_DIAGNOSIS=2015 38509   3840     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ YEAR_OF_DIAGNOSIS, data = data)

                YEAR_OF_DIAGNOSIS=2004 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  20534    1362    0.940 0.00159        0.937        0.943
   24  18897    1200    0.884 0.00216        0.880        0.888
   36  17618    1006    0.837 0.00251        0.832        0.841
   48  16580     775    0.799 0.00273        0.794        0.805
   60  15531     700    0.765 0.00290        0.760        0.771
  120   9840    2468    0.631 0.00344        0.624        0.638

                YEAR_OF_DIAGNOSIS=2005 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  22550    1409    0.943 0.00148        0.940        0.946
   24  20906    1253    0.890 0.00201        0.886        0.894
   36  19614    1045    0.845 0.00234        0.841        0.850
   48  18526     824    0.809 0.00255        0.804        0.814
   60  17333     754    0.776 0.00272        0.771        0.781
  120  10971    2625    0.646 0.00326        0.639        0.652

                YEAR_OF_DIAGNOSIS=2006 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  22924    1546    0.939 0.00151        0.936        0.942
   24  21237    1277    0.886 0.00203        0.882        0.890
   36  19737    1178    0.836 0.00237        0.832        0.841
   48  18347     997    0.794 0.00261        0.788        0.799
   60  17024     804    0.758 0.00277        0.753        0.764
  120   9515    2621    0.626 0.00330        0.619        0.632

                YEAR_OF_DIAGNOSIS=2007 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  23826    1705    0.935 0.00152        0.932        0.938
   24  21930    1425    0.879 0.00204        0.875        0.883
   36  20397    1196    0.830 0.00236        0.826        0.835
   48  19019     950    0.791 0.00256        0.786        0.796
   60  17522     746    0.760 0.00271        0.754        0.765
  120   4243    2625    0.621 0.00341        0.614        0.627

                YEAR_OF_DIAGNOSIS=2008 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  24610    1800    0.934 0.00150        0.931        0.937
   24  22521    1550    0.875 0.00203        0.871        0.879
   36  20830    1248    0.826 0.00234        0.821        0.830
   48  19203    1006    0.785 0.00255        0.780        0.790
   60  17682     802    0.752 0.00270        0.747        0.757
  120      3    2472    0.544 0.01943        0.507        0.583

                YEAR_OF_DIAGNOSIS=2009 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  25522    1877    0.934 0.00147        0.931        0.937
   24  23287    1598    0.875 0.00199        0.871        0.879
   36  21389    1246    0.827 0.00229        0.823        0.832
   48  19775    1009    0.788 0.00250        0.783        0.793
   60  18056     860    0.753 0.00266        0.748        0.758

                YEAR_OF_DIAGNOSIS=2010 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  25762    1960    0.932 0.00148        0.929        0.935
   24  23583    1498    0.877 0.00196        0.873        0.881
   36  21704    1283    0.829 0.00227        0.824        0.833
   48  19856    1007    0.789 0.00248        0.785        0.794
   60  17634     931    0.751 0.00266        0.746        0.756

                YEAR_OF_DIAGNOSIS=2011 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  26669    1936    0.935 0.00142        0.932        0.938
   24  24139    1711    0.874 0.00195        0.870        0.878
   36  21935    1353    0.824 0.00226        0.820        0.829
   48  19519    1101    0.782 0.00248        0.777        0.787
   60  15871     866    0.745 0.00267        0.739        0.750

                YEAR_OF_DIAGNOSIS=2012 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  27096    2010    0.934 0.00141        0.932        0.937
   24  24362    1591    0.878 0.00191        0.874        0.882
   36  21745    1319    0.829 0.00223        0.825        0.834
   48  18039    1008    0.789 0.00246        0.784        0.794
   60   7642     659    0.751 0.00277        0.746        0.757

                YEAR_OF_DIAGNOSIS=2013 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  28214    2136    0.934 0.00138        0.932        0.937
   24  24913    1639    0.878 0.00187        0.874        0.882
   36  20818    1294    0.830 0.00219        0.826        0.834
   48   9263     929    0.782 0.00259        0.777        0.788
   60      7     288    0.630 0.03382        0.567        0.700

                YEAR_OF_DIAGNOSIS=2014 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  28519    2126    0.936 0.00134        0.934        0.939
   24  23349    1530    0.883 0.00183        0.879        0.886
   36  10284    1074    0.831 0.00234        0.827        0.836
   48      7     343    0.653 0.04472        0.571        0.746

                YEAR_OF_DIAGNOSIS=2015 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  27931    2080    0.939 0.00129        0.937        0.942
   24  11935    1351    0.880 0.00201        0.876        0.884
   36      8     409    0.693 0.03072        0.635        0.756




   
## Univariable Cox Proportional Hazard Model for:  YEAR_OF_DIAGNOSIS
X matrix deemed to be singular; variable 12
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ YEAR_OF_DIAGNOSIS, data = data)

  n= 362910, number of events= 89972 

                          coef exp(coef) se(coef)      z Pr(>|z|)    
YEAR_OF_DIAGNOSIS2005 -0.02829   0.97210  0.01540 -1.838 0.066132 .  
YEAR_OF_DIAGNOSIS2006  0.04329   1.04424  0.01545  2.802 0.005084 ** 
YEAR_OF_DIAGNOSIS2007  0.05790   1.05961  0.01555  3.722 0.000197 ***
YEAR_OF_DIAGNOSIS2008  0.11329   1.11995  0.01557  7.276 3.45e-13 ***
YEAR_OF_DIAGNOSIS2009  0.10857   1.11469  0.01578  6.880 5.97e-12 ***
YEAR_OF_DIAGNOSIS2010  0.12528   1.13347  0.01605  7.807 5.88e-15 ***
YEAR_OF_DIAGNOSIS2011  0.15966   1.17311  0.01625  9.823  < 2e-16 ***
YEAR_OF_DIAGNOSIS2012  0.12014   1.12765  0.01688  7.119 1.09e-12 ***
YEAR_OF_DIAGNOSIS2013  0.14330   1.15408  0.01732  8.275  < 2e-16 ***
YEAR_OF_DIAGNOSIS2014  0.12010   1.12760  0.01849  6.494 8.38e-11 ***
YEAR_OF_DIAGNOSIS2015  0.12723   1.13568  0.02030  6.267 3.68e-10 ***
YEAR_OF_DIAGNOSIS2016       NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                      exp(coef) exp(-coef) lower .95 upper .95
YEAR_OF_DIAGNOSIS2005    0.9721     1.0287    0.9432     1.002
YEAR_OF_DIAGNOSIS2006    1.0442     0.9576    1.0131     1.076
YEAR_OF_DIAGNOSIS2007    1.0596     0.9437    1.0278     1.092
YEAR_OF_DIAGNOSIS2008    1.1200     0.8929    1.0863     1.155
YEAR_OF_DIAGNOSIS2009    1.1147     0.8971    1.0807     1.150
YEAR_OF_DIAGNOSIS2010    1.1335     0.8822    1.0984     1.170
YEAR_OF_DIAGNOSIS2011    1.1731     0.8524    1.1363     1.211
YEAR_OF_DIAGNOSIS2012    1.1277     0.8868    1.0910     1.166
YEAR_OF_DIAGNOSIS2013    1.1541     0.8665    1.1156     1.194
YEAR_OF_DIAGNOSIS2014    1.1276     0.8868    1.0875     1.169
YEAR_OF_DIAGNOSIS2015    1.1357     0.8805    1.0914     1.182
YEAR_OF_DIAGNOSIS2016        NA         NA        NA        NA

Concordance= 0.511  (se = 0.001 )
Rsquare= 0.001   (max possible= 0.998 )
Likelihood ratio test= 264  on 11 df,   p=0
Wald test            = 260  on 11 df,   p=0
Score (logrank) test = 260.3  on 11 df,   p=0
Removed 2 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  YEAR_OF_DIAGNOSIS
This manual palette can handle a maximum of 10 values. You have supplied 12.

Primary Site

uni_var(test_var = "SITE_TEXT", data_imp = data)
_________________________________________________
   
## SITE_TEXT
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SITE_TEXT, data = data)

                                                               n events median 0.95LCL 0.95UCL
SITE_TEXT=C44.0 Skin of lip, NOS                             631    187    155   118.2      NA
SITE_TEXT=C44.1 Eyelid                                      1184    380    122   109.5   139.3
SITE_TEXT=C44.2 External ear                               10639   3149    131   125.6   138.2
SITE_TEXT=C44.3 Skin of ear and unspecified parts of face  33680  10586    118   115.7   121.2
SITE_TEXT=C44.4 Skin of scalp and neck                     30276   9995    114   111.1   116.7
SITE_TEXT=C44.5 Skin of trunk                             113276  23471     NA      NA      NA
SITE_TEXT=C44.6 Skin of upper limb and shoulder            90064  19354    164   162.7      NA
SITE_TEXT=C44.7 Skin of lower limb and hip                 67737  12311     NA      NA      NA
SITE_TEXT=C44.8 Overlapping lesion of skin                   389    143    113    87.0      NA
SITE_TEXT=C44.9 Skin, NOS                                  15034  10396     11    10.6    11.5

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SITE_TEXT, data = data)

                SITE_TEXT=C44.0 Skin of lip, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    535      36    0.940 0.00971        0.921        0.959
   24    467      37    0.873 0.01394        0.846        0.901
   36    391      31    0.812 0.01673        0.780        0.845
   48    312      21    0.764 0.01871        0.728        0.802
   60    253      21    0.710 0.02085        0.670        0.752
  120     53      38    0.541 0.03086        0.484        0.605

                SITE_TEXT=C44.1 Eyelid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1026      60    0.947 0.00669        0.934        0.960
   24    862      78    0.872 0.01021        0.852        0.892
   36    714      55    0.813 0.01224        0.789        0.837
   48    582      49    0.754 0.01397        0.727        0.782
   60    473      42    0.696 0.01551        0.666        0.727
  120    105      86    0.503 0.02240        0.461        0.549

                SITE_TEXT=C44.2 External ear 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   9051     556    0.945 0.00229        0.940        0.949
   24   7649     612    0.878 0.00336        0.871        0.884
   36   6310     499    0.817 0.00409        0.809        0.825
   48   5107     412    0.760 0.00467        0.751        0.769
   60   4109     269    0.716 0.00510        0.707        0.727
  120    905     711    0.526 0.00766        0.511        0.541

                SITE_TEXT=C44.3 Skin of ear and unspecified parts of face 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  28750    1761    0.945 0.00128        0.942        0.947
   24  24359    2104    0.873 0.00192        0.869        0.876
   36  19836    1673    0.809 0.00233        0.804        0.813
   48  16060    1335    0.750 0.00266        0.745        0.756
   60  12693    1078    0.696 0.00294        0.690        0.702
  120   2658    2351    0.494 0.00437        0.486        0.503

                SITE_TEXT=C44.4 Skin of scalp and neck 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  25477    2029    0.929 0.00152        0.926        0.932
   24  21131    2204    0.845 0.00219        0.841        0.850
   36  16769    1763    0.770 0.00263        0.765        0.775
   48  13283    1211    0.710 0.00294        0.704        0.716
   60  10447     850    0.661 0.00319        0.654        0.667
  120   2165    1788    0.483 0.00459        0.474        0.492

                SITE_TEXT=C44.5 Skin of trunk 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  96604    4575    0.957 0.000622        0.956        0.958
   24  83748    4741    0.908 0.000911        0.906        0.910
   36  69838    3766    0.865 0.001110        0.862        0.867
   48  58021    2849    0.827 0.001267        0.824        0.829
   60  47338    2011    0.796 0.001397        0.793        0.798
  120  11762    4872    0.667 0.002168        0.662        0.671

                SITE_TEXT=C44.6 Skin of upper limb and shoulder 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  77412    3180    0.962 0.000654        0.961        0.964
   24  67145    3771    0.914 0.000994        0.912        0.916
   36  55975    3084    0.869 0.001230        0.866        0.871
   48  46240    2311    0.830 0.001414        0.827        0.833
   60  37565    1791    0.795 0.001577        0.792        0.798
  120   8927    4580    0.641 0.002558        0.636        0.646

                SITE_TEXT=C44.7 Skin of lower limb and hip 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  58436    2223    0.965 0.000727        0.964        0.967
   24  50972    2547    0.921 0.001096        0.919        0.923
   36  42817    2116    0.881 0.001359        0.878        0.883
   48  35910    1459    0.849 0.001549        0.845        0.852
   60  29399    1163    0.819 0.001724        0.815        0.822
  120   7556    2467    0.711 0.002634        0.706        0.717

                SITE_TEXT=C44.8 Overlapping lesion of skin 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    307      52    0.860  0.0180        0.826        0.896
   24    262      23    0.794  0.0213        0.754        0.837
   36    218      21    0.727  0.0240        0.682        0.776
   48    172      11    0.688  0.0254        0.640        0.740
   60    144       8    0.653  0.0269        0.603        0.708
  120     29      26    0.473  0.0388        0.402        0.555

                SITE_TEXT=C44.9 Skin, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   6559    7475    0.484 0.00418        0.476        0.492
   24   4464    1506    0.368 0.00411        0.360        0.376
   36   3211     643    0.311 0.00405        0.303        0.319
   48   2447     291    0.280 0.00403        0.273        0.288
   60   1881     177    0.258 0.00404        0.251        0.266
  120    412     270    0.201 0.00460        0.192        0.210




   
## Univariable Cox Proportional Hazard Model for:  SITE_TEXT

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SITE_TEXT, data = data)

  n= 362910, number of events= 89972 

                                                             coef exp(coef) se(coef)      z Pr(>|z|)    
SITE_TEXTC44.1 Eyelid                                     0.08145   1.08485  0.08933  0.912  0.36189    
SITE_TEXTC44.2 External ear                               0.02387   1.02416  0.07527  0.317  0.75110    
SITE_TEXTC44.3 Skin of ear and unspecified parts of face  0.09618   1.10095  0.07377  1.304  0.19233    
SITE_TEXTC44.4 Skin of scalp and neck                     0.19201   1.21168  0.07381  2.601  0.00928 ** 
SITE_TEXTC44.5 Skin of trunk                             -0.38193   0.68254  0.07342 -5.202 1.97e-07 ***
SITE_TEXTC44.6 Skin of upper limb and shoulder           -0.34458   0.70852  0.07348 -4.689 2.74e-06 ***
SITE_TEXTC44.7 Skin of lower limb and hip                -0.53944   0.58308  0.07368 -7.321 2.46e-13 ***
SITE_TEXTC44.8 Overlapping lesion of skin                 0.30726   1.35970  0.11109  2.766  0.00568 ** 
SITE_TEXTC44.9 Skin, NOS                                  1.67933   5.36195  0.07379 22.758  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                         exp(coef) exp(-coef) lower .95 upper .95
SITE_TEXTC44.1 Eyelid                                       1.0849     0.9218    0.9106    1.2924
SITE_TEXTC44.2 External ear                                 1.0242     0.9764    0.8837    1.1870
SITE_TEXTC44.3 Skin of ear and unspecified parts of face    1.1010     0.9083    0.9527    1.2722
SITE_TEXTC44.4 Skin of scalp and neck                       1.2117     0.8253    1.0485    1.4003
SITE_TEXTC44.5 Skin of trunk                                0.6825     1.4651    0.5911    0.7882
SITE_TEXTC44.6 Skin of upper limb and shoulder              0.7085     1.4114    0.6135    0.8183
SITE_TEXTC44.7 Skin of lower limb and hip                   0.5831     1.7150    0.5047    0.6737
SITE_TEXTC44.8 Overlapping lesion of skin                   1.3597     0.7355    1.0937    1.6904
SITE_TEXTC44.9 Skin, NOS                                    5.3620     0.1865    4.6399    6.1963

Concordance= 0.617  (se = 0.001 )
Rsquare= 0.07   (max possible= 0.998 )
Likelihood ratio test= 26455  on 9 df,   p=0
Wald test            = 38543  on 9 df,   p=0
Score (logrank) test = 51507  on 9 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SITE_TEXT

Histology

uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)
_________________________________________________
   
## HISTOLOGY_F_LIM
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISTOLOGY_F_LIM, data = data)

                           n events median 0.95LCL 0.95UCL
HISTOLOGY_F_LIM=8720  182616  49264  162.7   160.3   165.2
HISTOLOGY_F_LIM=8743  112470  17259     NA      NA      NA
HISTOLOGY_F_LIM=8742   19251   4953  129.7   125.6   133.9
HISTOLOGY_F_LIM=Other  48573  18496   95.9    93.1    97.9

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISTOLOGY_F_LIM, data = data)

                HISTOLOGY_F_LIM=8720 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 149938   14977    0.914 0.000673        0.913        0.915
   24 129130    9114    0.856 0.000861        0.855        0.858
   36 108073    6738    0.809 0.000988        0.807        0.811
   48  89985    4912    0.770 0.001088        0.767        0.772
   60  73642    3670    0.736 0.001175        0.733        0.738
  120  18210    8710    0.600 0.001712        0.597        0.603

                HISTOLOGY_F_LIM=8743 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  97178    2303    0.978 0.000454        0.977        0.979
   24  84928    3105    0.945 0.000726        0.944        0.947
   36  70760    2841    0.911 0.000939        0.909        0.913
   48  58608    2255    0.880 0.001115        0.878        0.882
   60  47353    1812    0.850 0.001278        0.848        0.853
  120  11407    4355    0.724 0.002196        0.720        0.728

                HISTOLOGY_F_LIM=8742 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  16438     630    0.965 0.00137        0.962        0.968
   24  14192     813    0.915 0.00215        0.911        0.919
   36  11594     790    0.860 0.00276        0.855        0.866
   48   9298     676    0.806 0.00329        0.800        0.812
   60   7377     506    0.758 0.00372        0.751        0.766
  120   1477    1368    0.530 0.00623        0.518        0.543

                HISTOLOGY_F_LIM=Other 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  40603    4037    0.913 0.00131        0.910        0.915
   24  32809    4591    0.806 0.00189        0.802        0.809
   36  25652    3282    0.720 0.00220        0.715        0.724
   48  20243    2106    0.656 0.00240        0.652        0.661
   60  15930    1422    0.607 0.00256        0.602        0.612
  120   3478    2756    0.447 0.00341        0.441        0.454




   
## Univariable Cox Proportional Hazard Model for:  HISTOLOGY_F_LIM
X matrix deemed to be singular; variable 1
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISTOLOGY_F_LIM, data = data)

  n= 362910, number of events= 89972 

                          coef exp(coef)  se(coef)       z Pr(>|z|)    
HISTOLOGY_F_LIM9680         NA        NA  0.000000      NA       NA    
HISTOLOGY_F_LIM8743  -0.609418  0.543667  0.008846 -68.895   <2e-16 ***
HISTOLOGY_F_LIM8742  -0.033942  0.966628  0.014909  -2.277   0.0228 *  
HISTOLOGY_F_LIMOther  0.439458  1.551866  0.008632  50.911   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                     exp(coef) exp(-coef) lower .95 upper .95
HISTOLOGY_F_LIM9680         NA         NA        NA        NA
HISTOLOGY_F_LIM8743     0.5437     1.8394    0.5343    0.5532
HISTOLOGY_F_LIM8742     0.9666     1.0345    0.9388    0.9953
HISTOLOGY_F_LIMOther    1.5519     0.6444    1.5258    1.5783

Concordance= 0.599  (se = 0.001 )
Rsquare= 0.028   (max possible= 0.998 )
Likelihood ratio test= 10352  on 3 df,   p=0
Wald test            = 9905  on 3 df,   p=0
Score (logrank) test = 10430  on 3 df,   p=0
Removed 2 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  HISTOLOGY_F_LIM

Grade

uni_var(test_var = "GRADE_F", data_imp = data)
_________________________________________________
   
## GRADE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ GRADE_F, data = data)

                                      n events median 0.95LCL 0.95UCL
GRADE_F=Gr I: Well Diff             759    178     NA   140.5      NA
GRADE_F=Gr II: Mod Diff            1064    243     NA   156.6      NA
GRADE_F=Gr III: Poor Diff          1877    975   46.8    40.9    55.1
GRADE_F=Gr IV: Undiff/Anaplastic    665    287   73.0    59.9   101.7
GRADE_F=NA/Unkown                358545  88289  164.5   161.9      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ GRADE_F, data = data)

                GRADE_F=Gr I: Well Diff 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    641      33    0.953 0.00793        0.938        0.969
   24    540      39    0.892 0.01203        0.869        0.916
   36    437      28    0.842 0.01464        0.814        0.871
   48    346      14    0.812 0.01620        0.781        0.844
   60    292      15    0.774 0.01816        0.739        0.810
  120     73      45    0.602 0.02816        0.549        0.660

                GRADE_F=Gr II: Mod Diff 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    925      41    0.959 0.00630        0.947        0.971
   24    814      53    0.902 0.00962        0.883        0.921
   36    695      36    0.860 0.01148        0.837        0.882
   48    600      22    0.831 0.01259        0.807        0.856
   60    506      22    0.799 0.01386        0.772        0.827
  120    140      64    0.654 0.02061        0.615        0.696

                GRADE_F=Gr III: Poor Diff 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1287     456    0.748  0.0102        0.728        0.768
   24    968     211    0.620  0.0117        0.598        0.644
   36    770     104    0.550  0.0122        0.527        0.574
   48    615      73    0.495  0.0126        0.471        0.520
   60    495      34    0.466  0.0128        0.441        0.491
  120    114      85    0.351  0.0151        0.322        0.382

                GRADE_F=Gr IV: Undiff/Anaplastic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    492     111    0.824  0.0152        0.795        0.854
   24    385      62    0.715  0.0185        0.680        0.752
   36    297      42    0.630  0.0204        0.592        0.672
   48    243      23    0.579  0.0214        0.539        0.623
   60    198      15    0.541  0.0221        0.500        0.586
  120     44      32    0.401  0.0289        0.348        0.462

                GRADE_F=NA/Unkown 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 300812   21306    0.937 0.000416        0.937        0.938
   24 258352   17258    0.881 0.000570        0.880        0.882
   36 213880   13441    0.832 0.000677        0.831        0.834
   48 176330    9817    0.791 0.000760        0.790        0.793
   60 142811    7324    0.756 0.000832        0.754        0.757
  120  34201   16963    0.614 0.001257        0.612        0.617




   
## Univariable Cox Proportional Hazard Model for:  GRADE_F
X matrix deemed to be singular; variable 4 5 6 7
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ GRADE_F, data = data)

  n= 362910, number of events= 89972 

                                    coef exp(coef) se(coef)      z Pr(>|z|)    
GRADE_FGr II: Mod Diff          -0.15501   0.85640  0.09866 -1.571    0.116    
GRADE_FGr III: Poor Diff         1.08725   2.96610  0.08151 13.339   <2e-16 ***
GRADE_FGr IV: Undiff/Anaplastic  0.81123   2.25069  0.09541  8.503   <2e-16 ***
GRADE_F5                              NA        NA  0.00000     NA       NA    
GRADE_F6                              NA        NA  0.00000     NA       NA    
GRADE_F7                              NA        NA  0.00000     NA       NA    
GRADE_F8                              NA        NA  0.00000     NA       NA    
GRADE_FNA/Unkown                 0.03965   1.04044  0.07503  0.528    0.597    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                exp(coef) exp(-coef) lower .95 upper .95
GRADE_FGr II: Mod Diff             0.8564     1.1677    0.7058     1.039
GRADE_FGr III: Poor Diff           2.9661     0.3371    2.5282     3.480
GRADE_FGr IV: Undiff/Anaplastic    2.2507     0.4443    1.8668     2.713
GRADE_F5                               NA         NA        NA        NA
GRADE_F6                               NA         NA        NA        NA
GRADE_F7                               NA         NA        NA        NA
GRADE_F8                               NA         NA        NA        NA
GRADE_FNA/Unkown                   1.0404     0.9611    0.8982     1.205

Concordance= 0.506  (se = 0 )
Rsquare= 0.003   (max possible= 0.998 )
Likelihood ratio test= 915.1  on 4 df,   p=0
Wald test            = 1234  on 4 df,   p=0
Score (logrank) test = 1343  on 4 df,   p=0
Removed 5 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  GRADE_F

Clinical T Stage

uni_var(test_var = "TNM_CLIN_T", data_imp = data)
_________________________________________________
   
## TNM_CLIN_T
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_T, data = data)

   9794 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
TNM_CLIN_T=N_A   1608    548  160.3   150.7      NA
TNM_CLIN_T=c0    4261   2744   14.1    12.7    15.3
TNM_CLIN_T=c1   18911   2882     NA      NA      NA
TNM_CLIN_T=c1A  55175   4305   95.5    95.5      NA
TNM_CLIN_T=c1B  20634   2001   97.1      NA      NA
TNM_CLIN_T=c2    5403   1306  153.7   141.8      NA
TNM_CLIN_T=c2A  34068   5945  164.4   162.0      NA
TNM_CLIN_T=c2B   8717   2535  110.8   104.6   122.2
TNM_CLIN_T=c3    3178   1210   89.6    81.8    96.4
TNM_CLIN_T=c3A  13377   3947  113.4   109.1   118.6
TNM_CLIN_T=c3B  10395   4261   69.7    66.3    72.0
TNM_CLIN_T=c4    2170   1190   41.9    37.9    45.3
TNM_CLIN_T=c4A   6281   2644   70.0    65.6    74.2
TNM_CLIN_T=c4B  10655   6264   34.2    32.9    35.4
TNM_CLIN_T=cX  155744  45656  165.4   164.6      NA
TNM_CLIN_T=pIS   2539    465     NA   157.9      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_T, data = data)

9794 observations deleted due to missingness 
                TNM_CLIN_T=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1405     113    0.928 0.00655        0.915        0.941
   24   1274      98    0.862 0.00883        0.845        0.880
   36   1170      67    0.816 0.00998        0.797        0.836
   48   1073      61    0.773 0.01089        0.752        0.794
   60    986      40    0.743 0.01142        0.721        0.766
  120    400     138    0.619 0.01375        0.593        0.647

                TNM_CLIN_T=c0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2014    1958    0.524 0.00783        0.509        0.540
   24   1383     415    0.410 0.00788        0.395        0.426
   36    980     177    0.353 0.00788        0.338        0.369
   48    726      84    0.320 0.00793        0.305        0.336
   60    530      40    0.300 0.00804        0.285        0.316
  120     87      59    0.242 0.00993        0.224        0.263

                TNM_CLIN_T=c1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  16419     403    0.977 0.00112        0.975        0.979
   24  14824     460    0.949 0.00170        0.946        0.952
   36  12980     464    0.918 0.00218        0.913        0.922
   48  11002     352    0.891 0.00253        0.886        0.896
   60   8896     316    0.863 0.00290        0.857        0.869
  120   1502     801    0.720 0.00579        0.709        0.732

                TNM_CLIN_T=c1A 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  45816     779    0.984 0.000555        0.983        0.986
   24  38151     945    0.963 0.000888        0.961        0.964
   36  28525     852    0.938 0.001198        0.936        0.941
   48  19945     721    0.910 0.001552        0.907        0.913
   60  12428     513    0.881 0.001964        0.878        0.885

                TNM_CLIN_T=c1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  17122     385    0.980 0.00103        0.978        0.982
   24  13908     510    0.948 0.00170        0.945        0.951
   36   9901     420    0.915 0.00229        0.910        0.919
   48   6635     305    0.881 0.00291        0.876        0.887
   60   3871     204    0.847 0.00367        0.840        0.854

                TNM_CLIN_T=c2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4665     211    0.959 0.00280        0.953        0.964
   24   4129     242    0.907 0.00417        0.899        0.915
   36   3503     212    0.858 0.00513        0.848        0.868
   48   2947     168    0.814 0.00587        0.803        0.826
   60   2326     161    0.766 0.00665        0.753        0.779
  120    303     287    0.592 0.01193        0.569        0.616

                TNM_CLIN_T=c2A 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  29464     759    0.976 0.000856        0.974        0.978
   24  25227    1136    0.936 0.001419        0.934        0.939
   36  20248    1037    0.894 0.001862        0.891        0.898
   48  16002     862    0.853 0.002255        0.848        0.857
   60  12260     680    0.812 0.002631        0.807        0.817
  120   1826    1343    0.645 0.005145        0.635        0.656

                TNM_CLIN_T=c2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7387     437    0.947 0.00249        0.942        0.951
   24   6074     590    0.867 0.00387        0.860        0.875
   36   4657     499    0.790 0.00485        0.780        0.799
   48   3501     345    0.725 0.00556        0.714        0.736
   60   2540     243    0.669 0.00619        0.657        0.681
  120    314     391    0.483 0.01043        0.463        0.503

                TNM_CLIN_T=c3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2678     250    0.917 0.00500        0.908        0.927
   24   2266     256    0.827 0.00700        0.814        0.841
   36   1815     232    0.738 0.00836        0.722        0.754
   48   1412     162    0.667 0.00922        0.650        0.686
   60   1070     114    0.609 0.00992        0.590        0.629
  120    129     188    0.425 0.01512        0.397        0.456

                TNM_CLIN_T=c3A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  11497     582    0.954 0.00187        0.950        0.957
   24   9564     878    0.878 0.00301        0.872        0.884
   36   7351     784    0.799 0.00383        0.792        0.807
   48   5649     573    0.731 0.00444        0.723        0.740
   60   4221     389    0.676 0.00492        0.666        0.685
  120    589     689    0.479 0.00826        0.463        0.495

                TNM_CLIN_T=c3B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   8723     818    0.917 0.00277        0.912        0.923
   24   6761    1160    0.789 0.00423        0.781        0.798
   36   4905     859    0.681 0.00503        0.671        0.690
   48   3581     530    0.600 0.00552        0.590        0.611
   60   2570     328    0.539 0.00591        0.528        0.551
  120    295     527    0.344 0.00888        0.327        0.362

                TNM_CLIN_T=c4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1605     425    0.797 0.00882        0.780        0.814
   24   1184     310    0.637 0.01075        0.616        0.659
   36    889     178    0.537 0.01140        0.515        0.560
   48    662     113    0.464 0.01175        0.441        0.487
   60    501      67    0.413 0.01199        0.390        0.437
  120     59      95    0.280 0.01600        0.250        0.313

                TNM_CLIN_T=c4A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5224     580    0.904 0.00380        0.896        0.911
   24   4043     712    0.775 0.00554        0.764        0.786
   36   3049     475    0.677 0.00641        0.665        0.690
   48   2280     334    0.597 0.00700        0.583        0.611
   60   1651     210    0.536 0.00745        0.521        0.551
  120    226     318    0.360 0.01061        0.339        0.381

                TNM_CLIN_T=c4B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   8023    2025    0.804 0.00392        0.796        0.811
   24   5505    1854    0.610 0.00493        0.600        0.620
   36   3705    1051    0.485 0.00522        0.475        0.495
   48   2521     581    0.401 0.00536        0.391        0.412
   60   1742     299    0.349 0.00546        0.338        0.359
  120    160     444    0.194 0.00736        0.180        0.209

                TNM_CLIN_T=cX 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 132133   11393    0.924 0.000686        0.923        0.925
   24 119023    7409    0.871 0.000881        0.869        0.873
   36 106890    5972    0.826 0.001009        0.824        0.828
   48  96220    4458    0.791 0.001097        0.788        0.793
   60  86053    3647    0.760 0.001168        0.757        0.762
  120  28530   11018    0.630 0.001528        0.627        0.633

                TNM_CLIN_T=pIS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2192      63    0.974 0.00329        0.967        0.980
   24   1850      88    0.932 0.00536        0.922        0.943
   36   1493      77    0.890 0.00697        0.876        0.903
   48   1164      66    0.847 0.00841        0.830        0.863
   60    892      49    0.807 0.00975        0.788        0.826
  120    152     115    0.623 0.01838        0.588        0.660




   
## Univariable Cox Proportional Hazard Model for:  TNM_CLIN_T
X matrix deemed to be singular; variable 8
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_T, data = data)

  n= 353116, number of events= 87903 
   (9794 observations deleted due to missingness)

                  coef exp(coef) se(coef)       z Pr(>|z|)    
TNM_CLIN_Tc0   1.70956   5.52651  0.04684  36.494  < 2e-16 ***
TNM_CLIN_Tc1  -0.57711   0.56152  0.04662 -12.379  < 2e-16 ***
TNM_CLIN_Tc1A -0.89914   0.40692  0.04543 -19.792  < 2e-16 ***
TNM_CLIN_Tc1B -0.63446   0.53022  0.04829 -13.138  < 2e-16 ***
TNM_CLIN_Tc2  -0.06001   0.94175  0.05092  -1.179    0.239    
TNM_CLIN_Tc2A -0.31573   0.72926  0.04467  -7.068 1.57e-12 ***
TNM_CLIN_Tc2B  0.29119   1.33802  0.04715   6.176 6.57e-10 ***
TNM_CLIN_Tc2C       NA        NA  0.00000      NA       NA    
TNM_CLIN_Tc3   0.51767   1.67811  0.05152  10.048  < 2e-16 ***
TNM_CLIN_Tc3A  0.26915   1.30885  0.04562   5.900 3.64e-09 ***
TNM_CLIN_Tc3B  0.71220   2.03847  0.04543  15.677  < 2e-16 ***
TNM_CLIN_Tc4   1.10491   3.01896  0.05166  21.387  < 2e-16 ***
TNM_CLIN_Tc4A  0.71881   2.05200  0.04698  15.302  < 2e-16 ***
TNM_CLIN_Tc4B  1.28103   3.60035  0.04461  28.714  < 2e-16 ***
TNM_CLIN_TcX  -0.05344   0.94796  0.04298  -1.243    0.214    
TNM_CLIN_TpIS -0.26369   0.76821  0.06307  -4.181 2.90e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
TNM_CLIN_Tc0     5.5265     0.1809    5.0417    6.0579
TNM_CLIN_Tc1     0.5615     1.7809    0.5125    0.6152
TNM_CLIN_Tc1A    0.4069     2.4575    0.3723    0.4448
TNM_CLIN_Tc1B    0.5302     1.8860    0.4823    0.5829
TNM_CLIN_Tc2     0.9418     1.0619    0.8523    1.0406
TNM_CLIN_Tc2A    0.7293     1.3713    0.6681    0.7960
TNM_CLIN_Tc2B    1.3380     0.7474    1.2199    1.4676
TNM_CLIN_Tc2C        NA         NA        NA        NA
TNM_CLIN_Tc3     1.6781     0.5959    1.5169    1.8564
TNM_CLIN_Tc3A    1.3089     0.7640    1.1969    1.4313
TNM_CLIN_Tc3B    2.0385     0.4906    1.8648    2.2283
TNM_CLIN_Tc4     3.0190     0.3312    2.7282    3.3407
TNM_CLIN_Tc4A    2.0520     0.4873    1.8715    2.2499
TNM_CLIN_Tc4B    3.6004     0.2778    3.2989    3.9293
TNM_CLIN_TcX     0.9480     1.0549    0.8714    1.0313
TNM_CLIN_TpIS    0.7682     1.3017    0.6789    0.8693

Concordance= 0.64  (se = 0.001 )
Rsquare= 0.066   (max possible= 0.998 )
Likelihood ratio test= 24210  on 15 df,   p=0
Wald test            = 28661  on 15 df,   p=0
Score (logrank) test = 34935  on 15 df,   p=0
Removed 2 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_CLIN_T
This manual palette can handle a maximum of 10 values. You have supplied 16.

Clinical N Stage

uni_var(test_var = "TNM_CLIN_N", data_imp = data)
_________________________________________________
   
## TNM_CLIN_N
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_N, data = data)

   8298 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
TNM_CLIN_N=N_A   1608    548  160.3   150.7      NA
TNM_CLIN_N=c0  247096  47331     NA   164.6      NA
TNM_CLIN_N=c1    5666   2838   43.6    40.5    47.0
TNM_CLIN_N=c1A   1325    516  110.8    93.4   131.8
TNM_CLIN_N=c1B   1624    954   28.6    26.1    33.0
TNM_CLIN_N=c2    1148    630   33.9    31.3    39.2
TNM_CLIN_N=c2A    415    186   77.7    67.0   118.9
TNM_CLIN_N=c2B    805    487   29.2    26.1    33.3
TNM_CLIN_N=c2C    900    479   38.7    35.5    45.7
TNM_CLIN_N=c3    2603   1825   15.7    14.8    17.1
TNM_CLIN_N=cX   91422  32280  155.9   153.6   158.2

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_N, data = data)

8298 observations deleted due to missingness 
                TNM_CLIN_N=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1405     113    0.928 0.00655        0.915        0.941
   24   1274      98    0.862 0.00883        0.845        0.880
   36   1170      67    0.816 0.00998        0.797        0.836
   48   1073      61    0.773 0.01089        0.752        0.794
   60    986      40    0.743 0.01142        0.721        0.766
  120    400     138    0.619 0.01375        0.593        0.647

                TNM_CLIN_N=c0 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 210265    8609    0.963 0.000394        0.962        0.964
   24 179841    9675    0.916 0.000595        0.915        0.917
   36 145477    8175    0.871 0.000746        0.870        0.873
   48 116031    6081    0.831 0.000870        0.830        0.833
   60  89945    4538    0.795 0.000983        0.794        0.797
  120  14585    9384    0.646 0.001775        0.643        0.650

                TNM_CLIN_N=c1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3995    1271    0.767 0.00574        0.756        0.778
   24   2926     691    0.628 0.00671        0.615        0.641
   36   2138     385    0.539 0.00714        0.525        0.553
   48   1585     206    0.483 0.00740        0.468        0.497
   60   1145     117    0.443 0.00766        0.428        0.458
  120    137     155    0.332 0.01086        0.311        0.354

                TNM_CLIN_N=c1A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1160      95    0.926 0.00731        0.912        0.940
   24    962     129    0.820 0.01092        0.799        0.842
   36    797      96    0.735 0.01278        0.710        0.760
   48    649      73    0.665 0.01396        0.638        0.693
   60    524      43    0.617 0.01475        0.589        0.646
  120     94      74    0.471 0.02050        0.432        0.513

                TNM_CLIN_N=c1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1085     442    0.719  0.0114        0.697        0.741
   24    769     247    0.550  0.0128        0.525        0.575
   36    553     115    0.462  0.0131        0.437        0.489
   48    419      71    0.399  0.0133        0.373        0.426
   60    330      22    0.376  0.0134        0.350        0.403
  120     50      52    0.278  0.0165        0.248        0.312

                TNM_CLIN_N=c2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    814     256    0.769  0.0127        0.744        0.794
   24    550     181    0.590  0.0152        0.561        0.621
   36    383      89    0.486  0.0160        0.456        0.519
   48    286      43    0.427  0.0164        0.396        0.461
   60    217      26    0.386  0.0167        0.354        0.420
  120     46      34    0.301  0.0194        0.265        0.341

                TNM_CLIN_N=c2A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    367      29    0.929  0.0128        0.904        0.954
   24    286      62    0.767  0.0214        0.726        0.810
   36    231      36    0.666  0.0244        0.620        0.716
   48    200      13    0.627  0.0252        0.580        0.679
   60    162      17    0.571  0.0264        0.522        0.626
  120     26      27    0.411  0.0371        0.344        0.490

                TNM_CLIN_N=c2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    576     199    0.748  0.0155        0.719        0.779
   24    394     138    0.562  0.0180        0.528        0.598
   36    287      73    0.454  0.0185        0.419        0.491
   48    216      36    0.393  0.0186        0.358        0.431
   60    160      16    0.361  0.0187        0.326        0.399
  120     25      25    0.264  0.0232        0.222        0.314

                TNM_CLIN_N=c2C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    687     155    0.822  0.0130        0.797        0.848
   24    482     141    0.646  0.0167        0.614        0.679
   36    332      82    0.527  0.0181        0.493        0.564
   48    220      44    0.450  0.0188        0.415        0.489
   60    153      23    0.396  0.0197        0.359        0.436
  120     21      33    0.281  0.0231        0.239        0.330

                TNM_CLIN_N=c3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1414    1077    0.576 0.00984        0.557        0.595
   24    851     433    0.391 0.00993        0.372        0.411
   36    570     154    0.314 0.00973        0.296        0.334
   48    388      85    0.263 0.00961        0.245        0.283
   60    279      27    0.243 0.00964        0.225        0.262
  120     31      45    0.168 0.01312        0.145        0.196

                TNM_CLIN_N=cX 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  75826    8985    0.898 0.00102        0.896        0.900
   24  67601    5322    0.834 0.00127        0.831        0.836
   36  60464    4113    0.782 0.00143        0.779        0.785
   48  54421    3020    0.742 0.00153        0.739        0.745
   60  48736    2439    0.707 0.00161        0.704        0.711
  120  19157    7130    0.577 0.00195        0.573        0.581




   
## Univariable Cox Proportional Hazard Model for:  TNM_CLIN_N

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_N, data = data)

  n= 354612, number of events= 88074 
   (8298 observations deleted due to missingness)

                  coef exp(coef) se(coef)      z Pr(>|z|)    
TNM_CLIN_Nc0  -0.23062   0.79404  0.04299 -5.365 8.12e-08 ***
TNM_CLIN_Nc1   1.07883   2.94122  0.04671 23.098  < 2e-16 ***
TNM_CLIN_Nc1A  0.43514   1.54518  0.06136  7.092 1.32e-12 ***
TNM_CLIN_Nc1B  1.26262   3.53466  0.05363 23.541  < 2e-16 ***
TNM_CLIN_Nc2   1.18426   3.26826  0.05845 20.262  < 2e-16 ***
TNM_CLIN_Nc2A  0.60849   1.83766  0.08487  7.170 7.52e-13 ***
TNM_CLIN_Nc2B  1.26659   3.54872  0.06231 20.328  < 2e-16 ***
TNM_CLIN_Nc2C  1.13924   3.12438  0.06259 18.201  < 2e-16 ***
TNM_CLIN_Nc3   1.76674   5.85177  0.04878 36.216  < 2e-16 ***
TNM_CLIN_NcX   0.14286   1.15357  0.04308  3.316 0.000913 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
TNM_CLIN_Nc0      0.794     1.2594    0.7299    0.8638
TNM_CLIN_Nc1      2.941     0.3400    2.6839    3.2232
TNM_CLIN_Nc1A     1.545     0.6472    1.3701    1.7426
TNM_CLIN_Nc1B     3.535     0.2829    3.1820    3.9265
TNM_CLIN_Nc2      3.268     0.3060    2.9145    3.6649
TNM_CLIN_Nc2A     1.838     0.5442    1.5560    2.1702
TNM_CLIN_Nc2B     3.549     0.2818    3.1408    4.0096
TNM_CLIN_Nc2C     3.124     0.3201    2.7637    3.5322
TNM_CLIN_Nc3      5.852     0.1709    5.3182    6.4389
TNM_CLIN_NcX      1.154     0.8669    1.0602    1.2552

Concordance= 0.59  (se = 0.001 )
Rsquare= 0.033   (max possible= 0.998 )
Likelihood ratio test= 11826  on 10 df,   p=0
Wald test            = 15960  on 10 df,   p=0
Score (logrank) test = 19080  on 10 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_CLIN_N
This manual palette can handle a maximum of 10 values. You have supplied 11.

Clinical Stage Group

uni_var(test_var = "TNM_CLIN_STAGE_GROUP", data_imp = data)
_________________________________________________
   
## TNM_CLIN_STAGE_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_STAGE_GROUP, data = data)

   43 observations deleted due to missingness 
                              n events median 0.95LCL 0.95UCL
TNM_CLIN_STAGE_GROUP=0     3415    657     NA  157.90      NA
TNM_CLIN_STAGE_GROUP=1    11136   1745     NA  161.58      NA
TNM_CLIN_STAGE_GROUP=1A   95569  10889     NA      NA      NA
TNM_CLIN_STAGE_GROUP=1B   63589   9853     NA  164.44      NA
TNM_CLIN_STAGE_GROUP=2     1978    608 151.26  117.19      NA
TNM_CLIN_STAGE_GROUP=2A   22432   6304 118.54  113.81   123.4
TNM_CLIN_STAGE_GROUP=2B   15534   6087  76.16   73.53    79.2
TNM_CLIN_STAGE_GROUP=2C    7948   4304  42.45   40.54    43.9
TNM_CLIN_STAGE_GROUP=3    11303   5144  61.27   58.78    64.8
TNM_CLIN_STAGE_GROUP=4    13516  11322   6.41    6.18     6.6
TNM_CLIN_STAGE_GROUP=N_A   1609    548 160.33  150.70      NA
TNM_CLIN_STAGE_GROUP=99  114838  32497 164.47  161.68      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_STAGE_GROUP, data = data)

43 observations deleted due to missingness 
                TNM_CLIN_STAGE_GROUP=0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2944     109    0.966 0.00319        0.960        0.972
   24   2494     122    0.924 0.00484        0.914        0.933
   36   2032     101    0.883 0.00609        0.871        0.895
   48   1633      88    0.842 0.00724        0.828        0.856
   60   1294      64    0.806 0.00822        0.790        0.822
  120    251     159    0.639 0.01461        0.611        0.668

                TNM_CLIN_STAGE_GROUP=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   9791     201    0.981 0.00134        0.978        0.983
   24   8870     279    0.952 0.00215        0.948        0.956
   36   7747     282    0.920 0.00279        0.915        0.926
   48   6595     210    0.893 0.00326        0.887        0.900
   60   5348     216    0.862 0.00380        0.854        0.869
  120   1070     504    0.726 0.00695        0.712        0.739

                TNM_CLIN_STAGE_GROUP=1A 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  82128    1263    0.986 0.000400        0.985        0.986
   24  72641    1639    0.965 0.000638        0.964        0.966
   36  61084    1654    0.941 0.000848        0.940        0.943
   48  50407    1480    0.917 0.001042        0.915        0.919
   60  40560    1201    0.893 0.001225        0.890        0.895
  120   7811    3257    0.766 0.002532        0.761        0.771

                TNM_CLIN_STAGE_GROUP=1B 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  54788    1190    0.980 0.000579        0.979        0.981
   24  47096    1807    0.946 0.000969        0.944        0.948
   36  37773    1718    0.908 0.001290        0.905        0.910
   48  29886    1417    0.870 0.001576        0.867        0.874
   60  22806    1149    0.833 0.001857        0.829        0.837
  120   3361    2356    0.670 0.003792        0.663        0.677

                TNM_CLIN_STAGE_GROUP=2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1705     102    0.946 0.00523        0.936        0.956
   24   1471     142    0.865 0.00807        0.849        0.881
   36   1244     112    0.796 0.00969        0.778        0.815
   48   1027      77    0.744 0.01073        0.723        0.765
   60    847      54    0.702 0.01154        0.680        0.725
  120     90     117    0.535 0.01850        0.500        0.572

                TNM_CLIN_STAGE_GROUP=2A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  19406     813    0.961 0.00133        0.959        0.964
   24  16245    1384    0.890 0.00223        0.885        0.894
   36  12639    1262    0.815 0.00288        0.809        0.820
   48   9653     943    0.748 0.00336        0.742        0.755
   60   7206     668    0.691 0.00376        0.684        0.699
  120   1006    1143    0.496 0.00632        0.484        0.509

                TNM_CLIN_STAGE_GROUP=2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  13273     965    0.935 0.00204        0.931        0.939
   24  10494    1611    0.816 0.00329        0.809        0.822
   36   7801    1267    0.710 0.00400        0.702        0.718
   48   5804     800    0.631 0.00443        0.622        0.639
   60   4177     544    0.565 0.00478        0.556        0.575
  120    528     841    0.368 0.00712        0.354        0.382

                TNM_CLIN_STAGE_GROUP=2C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   6406    1045    0.863 0.00393        0.856        0.871
   24   4562    1299    0.681 0.00547        0.670        0.691
   36   3142     831    0.548 0.00605        0.536        0.560
   48   2173     473    0.457 0.00634        0.445        0.470
   60   1524     246    0.400 0.00651        0.387        0.413
  120    148     399    0.220 0.00889        0.203        0.238

                TNM_CLIN_STAGE_GROUP=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   9131    1454    0.866 0.00327        0.860        0.873
   24   6869    1523    0.716 0.00443        0.707        0.725
   36   5137     884    0.617 0.00491        0.608        0.627
   48   3910     532    0.549 0.00519        0.539        0.559
   60   2945     283    0.505 0.00539        0.495        0.516
  120    465     439    0.379 0.00721        0.365        0.393

                TNM_CLIN_STAGE_GROUP=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4103    8748   0.3321 0.00415       0.3241       0.3403
   24   2140    1603   0.1967 0.00359       0.1898       0.2039
   36   1227     547   0.1411 0.00328       0.1348       0.1477
   48    804     199   0.1159 0.00315       0.1099       0.1223
   60    559      81   0.1027 0.00312       0.0968       0.1090
  120     82     130   0.0663 0.00353       0.0598       0.0736

                TNM_CLIN_STAGE_GROUP=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1406     113    0.928 0.00655        0.915        0.941
   24   1275      98    0.862 0.00882        0.845        0.880
   36   1171      67    0.816 0.00998        0.797        0.836
   48   1074      61    0.773 0.01088        0.752        0.795
   60    987      40    0.744 0.01142        0.722        0.766
  120    400     138    0.619 0.01375        0.593        0.647

                TNM_CLIN_STAGE_GROUP=99 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  99043    5937    0.946 0.000687        0.944        0.947
   24  86876    6115    0.885 0.000985        0.883        0.887
   36  75066    4926    0.833 0.001176        0.831        0.835
   48  65153    3668    0.790 0.001309        0.788        0.793
   60  56039    2862    0.754 0.001416        0.751        0.757
  120  19360    7703    0.614 0.001893        0.610        0.617




   
## Univariable Cox Proportional Hazard Model for:  TNM_CLIN_STAGE_GROUP
X matrix deemed to be singular; variable 4 10 11 12 14 15 16
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_STAGE_GROUP, data = data)

  n= 362867, number of events= 89958 
   (43 observations deleted due to missingness)

                            coef exp(coef) se(coef)       z Pr(>|z|)    
TNM_CLIN_STAGE_GROUP1   -0.34493   0.70827  0.04577  -7.535 4.86e-14 ***
TNM_CLIN_STAGE_GROUP1A  -0.58497   0.55712  0.04017 -14.561  < 2e-16 ***
TNM_CLIN_STAGE_GROUP1B  -0.18149   0.83403  0.04029  -4.504 6.67e-06 ***
TNM_CLIN_STAGE_GROUP1C        NA        NA  0.00000      NA       NA    
TNM_CLIN_STAGE_GROUP2    0.43921   1.55148  0.05628   7.805 6.00e-15 ***
TNM_CLIN_STAGE_GROUP2A   0.46369   1.58992  0.04100  11.310  < 2e-16 ***
TNM_CLIN_STAGE_GROUP2B   0.88353   2.41942  0.04107  21.513  < 2e-16 ***
TNM_CLIN_STAGE_GROUP2C   1.38281   3.98610  0.04190  33.004  < 2e-16 ***
TNM_CLIN_STAGE_GROUP3    1.08099   2.94759  0.04143  26.089  < 2e-16 ***
TNM_CLIN_STAGE_GROUP3A        NA        NA  0.00000      NA       NA    
TNM_CLIN_STAGE_GROUP3B        NA        NA  0.00000      NA       NA    
TNM_CLIN_STAGE_GROUP3C        NA        NA  0.00000      NA       NA    
TNM_CLIN_STAGE_GROUP4    2.83685  17.06185  0.04021  70.552  < 2e-16 ***
TNM_CLIN_STAGE_GROUP4A        NA        NA  0.00000      NA       NA    
TNM_CLIN_STAGE_GROUP4B        NA        NA  0.00000      NA       NA    
TNM_CLIN_STAGE_GROUP4C        NA        NA  0.00000      NA       NA    
TNM_CLIN_STAGE_GROUPN_A  0.22022   1.24635  0.05787   3.805 0.000142 ***
TNM_CLIN_STAGE_GROUP99   0.20140   1.22311  0.03942   5.110 3.23e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
TNM_CLIN_STAGE_GROUP1      0.7083    1.41189    0.6475    0.7748
TNM_CLIN_STAGE_GROUP1A     0.5571    1.79494    0.5149    0.6028
TNM_CLIN_STAGE_GROUP1B     0.8340    1.19900    0.7707    0.9026
TNM_CLIN_STAGE_GROUP1C         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP2      1.5515    0.64454    1.3895    1.7324
TNM_CLIN_STAGE_GROUP2A     1.5899    0.62896    1.4672    1.7230
TNM_CLIN_STAGE_GROUP2B     2.4194    0.41332    2.2323    2.6222
TNM_CLIN_STAGE_GROUP2C     3.9861    0.25087    3.6718    4.3273
TNM_CLIN_STAGE_GROUP3      2.9476    0.33926    2.7177    3.1969
TNM_CLIN_STAGE_GROUP3A         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP3B         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP3C         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP4     17.0619    0.05861   15.7689   18.4609
TNM_CLIN_STAGE_GROUP4A         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP4B         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP4C         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUPN_A    1.2464    0.80234    1.1127    1.3960
TNM_CLIN_STAGE_GROUP99     1.2231    0.81759    1.1322    1.3213

Concordance= 0.705  (se = 0.001 )
Rsquare= 0.149   (max possible= 0.998 )
Likelihood ratio test= 58580  on 11 df,   p=0
Wald test            = 83674  on 11 df,   p=0
Score (logrank) test = 142755  on 11 df,   p=0
Removed 8 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_CLIN_STAGE_GROUP
This manual palette can handle a maximum of 10 values. You have supplied 12.

Pathologic T Stage

uni_var(test_var = "TNM_PATH_T", data_imp = data)
_________________________________________________
   
## TNM_PATH_T
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_T, data = data)

   15174 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
TNM_PATH_T=N_A   1608    548  160.3   150.7      NA
TNM_PATH_T=p0    4729    966     NA   151.8      NA
TNM_PATH_T=p1   12061   1818     NA   161.4      NA
TNM_PATH_T=p1A  60788   4622     NA    95.5      NA
TNM_PATH_T=p1B  23363   2046   97.1      NA      NA
TNM_PATH_T=p2    3707    902     NA   156.0      NA
TNM_PATH_T=p2A  40964   6867  164.7   164.4      NA
TNM_PATH_T=p2B   9316   2469  130.2   119.3   133.8
TNM_PATH_T=p3    2393    906   97.6    87.9   113.3
TNM_PATH_T=p3A  17370   4845  120.4   116.7   127.2
TNM_PATH_T=p3B  12918   5054   73.5    70.7    77.7
TNM_PATH_T=p4    1628    874   45.3    42.2    50.5
TNM_PATH_T=p4A   9301   3754   69.8    66.8    73.7
TNM_PATH_T=p4B  15277   8722   34.9    33.9    35.8
TNM_PATH_T=pIS   1358    221     NA   151.2      NA
TNM_PATH_T=pX  130955  40263  165.4   164.6      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_T, data = data)

15174 observations deleted due to missingness 
                TNM_PATH_T=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1405     113    0.928 0.00655        0.915        0.941
   24   1274      98    0.862 0.00883        0.845        0.880
   36   1170      67    0.816 0.00998        0.797        0.836
   48   1073      61    0.773 0.01089        0.752        0.794
   60    986      40    0.743 0.01142        0.721        0.766
  120    400     138    0.619 0.01375        0.593        0.647

                TNM_PATH_T=p0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3692     425    0.905 0.00439        0.896        0.914
   24   2872     212    0.849 0.00555        0.838        0.860
   36   2069     123    0.808 0.00641        0.796        0.821
   48   1491      75    0.775 0.00721        0.761        0.789
   60    984      50    0.744 0.00818        0.728        0.760
  120    106      76    0.616 0.01797        0.582        0.652

                TNM_PATH_T=p1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  10570     226    0.980 0.00132        0.977        0.983
   24   9589     292    0.952 0.00206        0.948        0.956
   36   8462     272    0.924 0.00262        0.919        0.929
   48   7173     226    0.897 0.00308        0.891        0.903
   60   5859     225    0.867 0.00359        0.860        0.874
  120   1282     509    0.733 0.00672        0.720        0.746

                TNM_PATH_T=p1A 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  50621     847    0.985 0.000525        0.984        0.986
   24  41969    1022    0.963 0.000839        0.962        0.965
   36  31134     926    0.939 0.001136        0.937        0.941
   48  21579     758    0.912 0.001470        0.909        0.915
   60  13362     542    0.884 0.001871        0.880        0.887

                TNM_PATH_T=p1B 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  19493     383    0.982 0.000912        0.980        0.984
   24  15895     494    0.955 0.001490        0.952        0.958
   36  11326     457    0.923 0.002056        0.919        0.927
   48   7557     319    0.892 0.002628        0.887        0.897
   60   4426     199    0.863 0.003294        0.856        0.869

                TNM_PATH_T=p2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3243     119    0.966 0.00307        0.960        0.972
   24   2887     162    0.916 0.00481        0.907        0.925
   36   2540     133    0.872 0.00589        0.861        0.884
   48   2155     129    0.825 0.00687        0.812        0.839
   60   1810      95    0.787 0.00761        0.772        0.802
  120    379     242    0.617 0.01216        0.594        0.641

                TNM_PATH_T=p2A 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  35584     816    0.979 0.000739        0.977        0.980
   24  30537    1263    0.942 0.001240        0.940        0.944
   36  24466    1213    0.901 0.001651        0.898        0.904
   48  19261     993    0.861 0.002012        0.857        0.865
   60  14662     774    0.822 0.002357        0.818        0.827
  120   2789    1619    0.659 0.004441        0.650        0.668

                TNM_PATH_T=p2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7937     385    0.956 0.00221        0.951        0.960
   24   6614     519    0.890 0.00346        0.883        0.897
   36   5091     508    0.815 0.00449        0.806        0.824
   48   3855     357    0.752 0.00525        0.742        0.763
   60   2830     225    0.703 0.00584        0.692        0.715
  120    494     426    0.516 0.00968        0.498        0.536

                TNM_PATH_T=p3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2050     149    0.935 0.00518        0.925        0.945
   24   1754     187    0.847 0.00769        0.832        0.862
   36   1426     180    0.757 0.00938        0.738        0.775
   48   1168     118    0.691 0.01035        0.671        0.711
   60    905     108    0.622 0.01125        0.600        0.644
  120    170     155    0.453 0.01541        0.424        0.484

                TNM_PATH_T=p3A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  15063     643    0.961 0.00152        0.958        0.964
   24  12546    1081    0.889 0.00254        0.884        0.894
   36   9650     968    0.814 0.00327        0.807        0.820
   48   7382     689    0.750 0.00381        0.743        0.758
   60   5437     515    0.692 0.00430        0.684        0.700
  120   1026     872    0.502 0.00686        0.489        0.515

                TNM_PATH_T=p3B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  11009     866    0.930 0.00231        0.925        0.934
   24   8564    1397    0.806 0.00368        0.799        0.813
   36   6167    1106    0.693 0.00448        0.684        0.702
   48   4485     613    0.617 0.00492        0.608        0.627
   60   3163     405    0.555 0.00532        0.544        0.565
  120    512     619    0.369 0.00776        0.354        0.385

                TNM_PATH_T=p4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1274     264    0.833 0.00941        0.814        0.851
   24    967     218    0.685 0.01193        0.662        0.709
   36    747     143    0.580 0.01296        0.555        0.606
   48    556     117    0.484 0.01351        0.458        0.511
   60    421      59    0.428 0.01378        0.402        0.456
  120     74      65    0.313 0.01702        0.282        0.348

                TNM_PATH_T=p4A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7768     793    0.911 0.00303        0.905        0.917
   24   5923    1064    0.779 0.00454        0.771        0.788
   36   4366     706    0.679 0.00531        0.669        0.689
   48   3217     452    0.602 0.00581        0.591        0.614
   60   2294     285    0.542 0.00623        0.530        0.555
  120    399     429    0.374 0.00873        0.357        0.392

                TNM_PATH_T=p4B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  11675    2691    0.817 0.00319        0.811        0.824
   24   7938    2677    0.621 0.00411        0.613        0.629
   36   5215    1542    0.490 0.00440        0.482        0.499
   48   3501     788    0.408 0.00454        0.399        0.417
   60   2366     415    0.354 0.00466        0.345        0.363
  120    298     578    0.208 0.00610        0.197        0.220

                TNM_PATH_T=pIS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1160      22    0.982 0.00372        0.975        0.990
   24    990      37    0.949 0.00647        0.937        0.962
   36    777      42    0.904 0.00921        0.886        0.922
   48    612      31    0.865 0.01118        0.843        0.887
   60    459      22    0.829 0.01303        0.804        0.855
  120    101      64    0.632 0.02541        0.584        0.684

                TNM_PATH_T=pX 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 111129   10285    0.919 0.000770        0.917        0.920
   24 102135    5970    0.868 0.000963        0.867        0.870
   36  94648    4783    0.827 0.001087        0.825        0.829
   48  87842    3875    0.793 0.001174        0.791        0.795
   60  80833    3231    0.763 0.001241        0.761        0.765
  120  26542   10481    0.634 0.001591        0.631        0.637




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_T

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_T, data = data)

  n= 347736, number of events= 84877 
   (15174 observations deleted due to missingness)

                  coef exp(coef) se(coef)       z Pr(>|z|)    
TNM_PATH_Tp0   0.11831   1.12559  0.05353   2.210 0.027086 *  
TNM_PATH_Tp1  -0.61500   0.54064  0.04875 -12.616  < 2e-16 ***
TNM_PATH_Tp1A -0.90762   0.40348  0.04526 -20.054  < 2e-16 ***
TNM_PATH_Tp1B -0.73029   0.48177  0.04818 -15.157  < 2e-16 ***
TNM_PATH_Tp2  -0.13147   0.87680  0.05417  -2.427 0.015228 *  
TNM_PATH_Tp2A -0.36182   0.69641  0.04442  -8.146 3.33e-16 ***
TNM_PATH_Tp2B  0.17705   1.19368  0.04725   3.747 0.000179 ***
TNM_PATH_Tp3   0.44268   1.55688  0.05414   8.177 3.33e-16 ***
TNM_PATH_Tp3A  0.20590   1.22863  0.04510   4.565 4.99e-06 ***
TNM_PATH_Tp3B  0.65706   1.92910  0.04502  14.595  < 2e-16 ***
TNM_PATH_Tp4   1.00597   2.73456  0.05452  18.452  < 2e-16 ***
TNM_PATH_Tp4A  0.70111   2.01599  0.04577  15.317  < 2e-16 ***
TNM_PATH_Tp4B  1.27041   3.56231  0.04411  28.800  < 2e-16 ***
TNM_PATH_TpIS -0.37452   0.68762  0.07970  -4.699 2.61e-06 ***
TNM_PATH_TpX  -0.06880   0.93351  0.04301  -1.600 0.109646    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_Tp0     1.1256     0.8884    1.0135    1.2501
TNM_PATH_Tp1     0.5406     1.8497    0.4914    0.5948
TNM_PATH_Tp1A    0.4035     2.4784    0.3692    0.4409
TNM_PATH_Tp1B    0.4818     2.0757    0.4384    0.5295
TNM_PATH_Tp2     0.8768     1.1405    0.7885    0.9750
TNM_PATH_Tp2A    0.6964     1.4359    0.6383    0.7598
TNM_PATH_Tp2B    1.1937     0.8377    1.0881    1.3095
TNM_PATH_Tp3     1.5569     0.6423    1.4001    1.7311
TNM_PATH_Tp3A    1.2286     0.8139    1.1247    1.3422
TNM_PATH_Tp3B    1.9291     0.5184    1.7662    2.1071
TNM_PATH_Tp4     2.7346     0.3657    2.4574    3.0429
TNM_PATH_Tp4A    2.0160     0.4960    1.8430    2.2052
TNM_PATH_Tp4B    3.5623     0.2807    3.2673    3.8840
TNM_PATH_TpIS    0.6876     1.4543    0.5882    0.8039
TNM_PATH_TpX     0.9335     1.0712    0.8580    1.0156

Concordance= 0.641  (se = 0.001 )
Rsquare= 0.063   (max possible= 0.997 )
Likelihood ratio test= 22560  on 15 df,   p=0
Wald test            = 25245  on 15 df,   p=0
Score (logrank) test = 29762  on 15 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_T
This manual palette can handle a maximum of 10 values. You have supplied 16.

Pathologic N Stage

uni_var(test_var = "TNM_PATH_N", data_imp = data)
_________________________________________________
   
## TNM_PATH_N
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_N, data = data)

   28869 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
TNM_PATH_N=N_A   1608    548  160.3   150.7      NA
TNM_PATH_N=p0  161766  28438     NA   165.4      NA
TNM_PATH_N=p1    4601   1864   85.2    76.4    94.5
TNM_PATH_N=p1A  10736   2992  134.1   127.5   148.5
TNM_PATH_N=p1B   2516   1177   49.1    43.1    55.5
TNM_PATH_N=p2    1370    679   50.8    45.8    59.0
TNM_PATH_N=p2A   3523   1289   79.7    73.7    91.1
TNM_PATH_N=p2B   1858    913   41.5    37.5    47.4
TNM_PATH_N=p2C   1798    860   48.8    43.7    54.3
TNM_PATH_N=p3    4889   3056   25.0    23.9    26.2
TNM_PATH_N=pX  139376  41237  161.3   159.6   164.5

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_N, data = data)

28869 observations deleted due to missingness 
                TNM_PATH_N=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1405     113    0.928 0.00655        0.915        0.941
   24   1274      98    0.862 0.00883        0.845        0.880
   36   1170      67    0.816 0.00998        0.797        0.836
   48   1073      61    0.773 0.01089        0.752        0.794
   60    986      40    0.743 0.01142        0.721        0.766
  120    400     138    0.619 0.01375        0.593        0.647

                TNM_PATH_N=p0 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 139818    3568    0.976 0.000392        0.976        0.977
   24 121377    5311    0.937 0.000645        0.936        0.939
   36  99735    5080    0.895 0.000846        0.893        0.897
   48  80892    3840    0.858 0.001004        0.856        0.860
   60  63553    3057    0.822 0.001152        0.820        0.824
  120  13706    6754    0.674 0.002023        0.670        0.677

                TNM_PATH_N=p1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3778     490    0.889 0.00474        0.880        0.898
   24   3001     479    0.772 0.00647        0.759        0.784
   36   2337     353    0.676 0.00741        0.662        0.691
   48   1832     210    0.612 0.00793        0.596        0.627
   60   1380     128    0.564 0.00837        0.548        0.580
  120    247     183    0.437 0.01134        0.415        0.459

                TNM_PATH_N=p1A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   9435     407    0.960 0.00194        0.956        0.964
   24   7696     796    0.875 0.00338        0.868        0.882
   36   5840     632    0.797 0.00429        0.788        0.805
   48   4401     437    0.731 0.00495        0.722        0.741
   60   3208     279    0.680 0.00549        0.669        0.691
  120    595     405    0.529 0.00857        0.512        0.546

                TNM_PATH_N=p1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1945     381    0.842 0.00746        0.827        0.856
   24   1400     365    0.676 0.00982        0.657        0.696
   36    968     216    0.563 0.01079        0.542        0.585
   48    692      92    0.504 0.01131        0.482        0.526
   60    490      52    0.461 0.01181        0.438        0.485
  120     85      65    0.354 0.01574        0.324        0.386

                TNM_PATH_N=p2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1121     168    0.873 0.00915        0.855        0.891
   24    834     199    0.712 0.01274        0.688        0.738
   36    635     121    0.604 0.01411        0.577        0.632
   48    475      90    0.514 0.01489        0.485        0.544
   60    366      43    0.463 0.01529        0.434        0.494
  120     75      48    0.364 0.01879        0.329        0.402

                TNM_PATH_N=p2A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3059     188    0.944 0.00398        0.936        0.952
   24   2377     392    0.817 0.00690        0.804        0.831
   36   1700     293    0.707 0.00846        0.691        0.724
   48   1247     166    0.632 0.00936        0.614        0.651
   60    890      95    0.579 0.01007        0.559        0.599
  120    162     149    0.413 0.01495        0.385        0.444

                TNM_PATH_N=p2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1442     287    0.840 0.00867        0.823        0.857
   24    991     305    0.653 0.01161        0.631        0.677
   36    668     165    0.535 0.01268        0.511        0.560
   48    463      74    0.469 0.01325        0.444        0.496
   60    320      33    0.430 0.01377        0.404        0.458
  120     61      43    0.329 0.01833        0.295        0.367

                TNM_PATH_N=p2C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1427     227    0.868 0.00817        0.852        0.884
   24   1004     275    0.692 0.01151        0.670        0.715
   36    693     144    0.583 0.01281        0.559        0.609
   48    479      87    0.503 0.01367        0.477        0.530
   60    337      51    0.443 0.01438        0.416        0.473
  120     58      73    0.287 0.01891        0.253        0.327

                TNM_PATH_N=p3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3417    1294    0.730 0.00643        0.717        0.742
   24   2133     970    0.513 0.00740        0.498        0.527
   36   1404     407    0.407 0.00752        0.393        0.422
   48    930     191    0.345 0.00761        0.330        0.360
   60    656      81    0.311 0.00774        0.297        0.327
  120    100     102    0.231 0.00959        0.213        0.250

                TNM_PATH_N=pX 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 115671   11418    0.914 0.000766        0.913        0.916
   24 101946    6977    0.858 0.000975        0.856        0.860
   36  88685    5346    0.811 0.001113        0.809        0.813
   48  77042    4131    0.771 0.001217        0.769        0.774
   60  66676    3203    0.738 0.001301        0.735        0.740
  120  19083    8917    0.600 0.001758        0.596        0.603




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_N

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_N, data = data)

  n= 334041, number of events= 83053 
   (28869 observations deleted due to missingness)

                  coef exp(coef) se(coef)      z Pr(>|z|)    
TNM_PATH_Np0  -0.35771   0.69927  0.04315 -8.290  < 2e-16 ***
TNM_PATH_Np1   0.63924   1.89504  0.04862 13.147  < 2e-16 ***
TNM_PATH_Np1A  0.22139   1.24781  0.04650  4.761 1.92e-06 ***
TNM_PATH_Np1B  0.98521   2.67836  0.05176 19.034  < 2e-16 ***
TNM_PATH_Np2   0.89237   2.44091  0.05745 15.533  < 2e-16 ***
TNM_PATH_Np2A  0.57084   1.76975  0.05103 11.186  < 2e-16 ***
TNM_PATH_Np2B  1.06589   2.90343  0.05409 19.708  < 2e-16 ***
TNM_PATH_Np2C  1.00466   2.73098  0.05470 18.365  < 2e-16 ***
TNM_PATH_Np3   1.46776   4.33951  0.04646 31.591  < 2e-16 ***
TNM_PATH_NpX   0.05819   1.05991  0.04301  1.353    0.176    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_Np0     0.6993     1.4301    0.6426     0.761
TNM_PATH_Np1     1.8950     0.5277    1.7228     2.085
TNM_PATH_Np1A    1.2478     0.8014    1.1391     1.367
TNM_PATH_Np1B    2.6784     0.3734    2.4200     2.964
TNM_PATH_Np2     2.4409     0.4097    2.1810     2.732
TNM_PATH_Np2A    1.7698     0.5651    1.6013     1.956
TNM_PATH_Np2B    2.9034     0.3444    2.6114     3.228
TNM_PATH_Np2C    2.7310     0.3662    2.4533     3.040
TNM_PATH_Np3     4.3395     0.2304    3.9618     4.753
TNM_PATH_NpX     1.0599     0.9435    0.9742     1.153

Concordance= 0.604  (se = 0.001 )
Rsquare= 0.036   (max possible= 0.998 )
Likelihood ratio test= 12120  on 10 df,   p=0
Wald test            = 15069  on 10 df,   p=0
Score (logrank) test = 17350  on 10 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_N
This manual palette can handle a maximum of 10 values. You have supplied 11.

Pathologic M Stage

uni_var(test_var = "TNM_PATH_M", data_imp = data)
_________________________________________________
   
## TNM_PATH_M
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_M, data = data)

   199448 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
TNM_PATH_M=N_A   1609    549 160.33  150.70      NA
TNM_PATH_M=p1    1989   1731   7.43    6.97    8.08
TNM_PATH_M=p1A   1165    718  23.13   20.50   26.41
TNM_PATH_M=p1B    907    704  10.55    9.43   12.12
TNM_PATH_M=p1C   2241   1898   5.88    5.49    6.34
TNM_PATH_M=pX  155551  49114 165.19  164.47      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_M, data = data)

199448 observations deleted due to missingness 
                TNM_PATH_M=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1406     113    0.928 0.00655        0.915        0.941
   24   1275      98    0.862 0.00882        0.845        0.880
   36   1171      67    0.816 0.00998        0.797        0.836
   48   1074      61    0.773 0.01088        0.752        0.795
   60    986      41    0.743 0.01143        0.721        0.766
  120    400     138    0.619 0.01375        0.592        0.646

                TNM_PATH_M=p1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    669    1261   0.3534 0.01087       0.3327       0.3753
   24    379     265   0.2105 0.00938       0.1929       0.2297
   36    260      98   0.1546 0.00842       0.1389       0.1720
   48    205      39   0.1305 0.00795       0.1158       0.1470
   60    172      20   0.1173 0.00767       0.1032       0.1334
  120     43      41   0.0833 0.00725       0.0702       0.0988

                TNM_PATH_M=p1A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    711     362    0.675  0.0141        0.647        0.703
   24    466     184    0.492  0.0154        0.463        0.523
   36    315      89    0.389  0.0156        0.360        0.421
   48    239      33    0.345  0.0156        0.316        0.377
   60    191      13    0.325  0.0157        0.295        0.357
  120     39      34    0.231  0.0187        0.197        0.271

                TNM_PATH_M=p1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    396     468    0.470  0.0169       0.4376        0.504
   24    215     138    0.299  0.0159       0.2691        0.331
   36    107      59    0.206  0.0149       0.1788        0.237
   48     65      20    0.163  0.0146       0.1364        0.194
   60     41      10    0.134  0.0146       0.1087        0.166
  120      7       7    0.102  0.0155       0.0759        0.138

                TNM_PATH_M=p1C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    610    1536   0.2964 0.00986       0.2777       0.3164
   24    289     246   0.1695 0.00836       0.1539       0.1867
   36    154      71   0.1217 0.00773       0.1075       0.1379
   48     96      21   0.1036 0.00753       0.0899       0.1195
   60     59      13   0.0880 0.00756       0.0743       0.1041
  120      4      11   0.0607 0.00900       0.0454       0.0811

                TNM_PATH_M=pX 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 137506    7936    0.947 0.000578        0.946        0.948
   24 126905    7769    0.893 0.000808        0.891        0.895
   36 117977    6697    0.845 0.000951        0.844        0.847
   48 110012    5434    0.806 0.001046        0.804        0.808
   60 101841    4589    0.772 0.001117        0.770        0.774
  120  34079   14529    0.631 0.001437        0.628        0.634




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_M

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_M, data = data)

  n= 163462, number of events= 54714 
   (199448 observations deleted due to missingness)

                  coef exp(coef) se(coef)      z Pr(>|z|)    
TNM_PATH_Mp1   2.28750   9.85031  0.04910 46.590   <2e-16 ***
TNM_PATH_Mp1A  1.44157   4.22733  0.05676 25.400   <2e-16 ***
TNM_PATH_Mp1B  2.18953   8.93099  0.05710 38.346   <2e-16 ***
TNM_PATH_Mp1C  2.64046  14.01966  0.04874 54.173   <2e-16 ***
TNM_PATH_MpX  -0.09322   0.91099  0.04292 -2.172   0.0298 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_Mp1      9.850    0.10152    8.9466   10.8453
TNM_PATH_Mp1A     4.227    0.23656    3.7823    4.7247
TNM_PATH_Mp1B     8.931    0.11197    7.9854    9.9885
TNM_PATH_Mp1C    14.020    0.07133   12.7423   15.4250
TNM_PATH_MpX      0.911    1.09771    0.8375    0.9909

Concordance= 0.554  (se = 0 )
Rsquare= 0.083   (max possible= 1 )
Likelihood ratio test= 14212  on 5 df,   p=0
Wald test            = 24732  on 5 df,   p=0
Score (logrank) test = 40425  on 5 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_M

Pathologic Stage Group

uni_var(test_var = "TNM_PATH_STAGE_GROUP", data_imp = data)
_________________________________________________
   
## TNM_PATH_STAGE_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_STAGE_GROUP, data = data)

   11146 observations deleted due to missingness 
                             n events median 0.95LCL 0.95UCL
TNM_PATH_STAGE_GROUP=0    2692    446     NA  148.21      NA
TNM_PATH_STAGE_GROUP=1    8007   1254     NA      NA      NA
TNM_PATH_STAGE_GROUP=1A  86795  10430     NA      NA      NA
TNM_PATH_STAGE_GROUP=1B  70294  10651     NA      NA      NA
TNM_PATH_STAGE_GROUP=2    1642    519 150.54  129.41      NA
TNM_PATH_STAGE_GROUP=2A  24145   6449 135.36  132.04  142.42
TNM_PATH_STAGE_GROUP=2B  16599   6174  89.26   86.54   92.39
TNM_PATH_STAGE_GROUP=2C   8258   4301  47.90   46.59   49.71
TNM_PATH_STAGE_GROUP=3    6964   3123  82.40   76.25   88.18
TNM_PATH_STAGE_GROUP=3A  10302   2803 154.78  139.93      NA
TNM_PATH_STAGE_GROUP=3B   9390   3943  67.78   64.76   71.85
TNM_PATH_STAGE_GROUP=3C   6606   3825  31.38   29.96   32.59
TNM_PATH_STAGE_GROUP=4    7652   6190   8.51    8.08    8.87
TNM_PATH_STAGE_GROUP=N_A  1609    548 160.33  150.70      NA
TNM_PATH_STAGE_GROUP=99  90809  26192 160.03  155.70  161.68

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_STAGE_GROUP, data = data)

11146 observations deleted due to missingness 
                TNM_PATH_STAGE_GROUP=0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2302      55    0.978 0.00293        0.972        0.984
   24   1970      76    0.944 0.00477        0.935        0.953
   36   1548      71    0.906 0.00640        0.893        0.918
   48   1255      51    0.874 0.00759        0.859        0.889
   60    963      46    0.838 0.00893        0.821        0.856
  120    229     132    0.649 0.01718        0.616        0.683

                TNM_PATH_STAGE_GROUP=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7088     124    0.984 0.00147        0.981        0.986
   24   6501     163    0.960 0.00231        0.956        0.965
   36   5753     179    0.932 0.00303        0.927        0.938
   48   4996     155    0.906 0.00362        0.899        0.913
   60   4158     153    0.876 0.00423        0.868        0.884
  120   1208     414    0.743 0.00733        0.728        0.757

                TNM_PATH_STAGE_GROUP=1A 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  75125    1076    0.987 0.000405        0.986        0.987
   24  67214    1425    0.967 0.000649        0.966        0.968
   36  57766    1448    0.945 0.000857        0.943        0.947
   48  48907    1333    0.921 0.001049        0.919        0.924
   60  40676    1125    0.899 0.001226        0.896        0.901
  120  10493    3459    0.775 0.002349        0.771        0.780

                TNM_PATH_STAGE_GROUP=1B 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  61234    1018    0.984 0.000483        0.984        0.985
   24  53754    1595    0.957 0.000816        0.956        0.959
   36  44573    1750    0.924 0.001116        0.922        0.926
   48  36505    1459    0.891 0.001368        0.888        0.894
   60  29141    1277    0.857 0.001617        0.854        0.860
  120   6648    3134    0.705 0.002983        0.699        0.711

                TNM_PATH_STAGE_GROUP=2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1459      53    0.966 0.00459        0.957        0.975
   24   1299      97    0.900 0.00774        0.885        0.915
   36   1128      95    0.833 0.00979        0.814        0.852
   48    992      60    0.787 0.01090        0.766        0.808
   60    835      66    0.731 0.01207        0.708        0.755
  120    194     138    0.552 0.01709        0.519        0.586

                TNM_PATH_STAGE_GROUP=2A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  21108     658    0.971 0.00112        0.969        0.973
   24  18179    1178    0.915 0.00191        0.911        0.918
   36  14677    1219    0.849 0.00254        0.844        0.854
   48  11757     938    0.790 0.00300        0.784        0.796
   60   9236     743    0.736 0.00339        0.729        0.743
  120   1941    1556    0.541 0.00527        0.530        0.551

                TNM_PATH_STAGE_GROUP=2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  14411     818    0.948 0.00176        0.945        0.952
   24  11762    1445    0.849 0.00294        0.843        0.855
   36   8989    1310    0.748 0.00369        0.740        0.755
   48   6954     833    0.673 0.00413        0.665        0.681
   60   5273     570    0.613 0.00447        0.604        0.622
  120   1011    1093    0.416 0.00621        0.404        0.428

                TNM_PATH_STAGE_GROUP=2C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   6869     843    0.893 0.00347        0.887        0.900
   24   5068    1242    0.725 0.00515        0.715        0.735
   36   3608     843    0.596 0.00586        0.585        0.608
   48   2546     541    0.499 0.00623        0.487        0.511
   60   1855     297    0.435 0.00644        0.423        0.448
  120    278     505    0.250 0.00800        0.235        0.266

                TNM_PATH_STAGE_GROUP=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5915     618    0.908 0.00354        0.901        0.915
   24   4706     884    0.768 0.00526        0.758        0.778
   36   3784     567    0.672 0.00596        0.660        0.684
   48   3104     348    0.607 0.00631        0.595        0.620
   60   2561     223    0.561 0.00655        0.548        0.574
  120    603     431    0.427 0.00788        0.412        0.443

                TNM_PATH_STAGE_GROUP=3A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   9201     286    0.971 0.00170        0.968        0.974
   24   7665     693    0.894 0.00321        0.888        0.901
   36   6013     586    0.821 0.00415        0.812        0.829
   48   4701     442    0.756 0.00484        0.746        0.765
   60   3593     289    0.705 0.00537        0.694        0.715
  120    754     466    0.554 0.00804        0.539        0.570

                TNM_PATH_STAGE_GROUP=3B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7993     705    0.921 0.00285        0.916        0.927
   24   6101    1172    0.780 0.00450        0.771        0.789
   36   4439     817    0.668 0.00531        0.657        0.678
   48   3308     461    0.593 0.00575        0.581        0.604
   60   2367     308    0.532 0.00613        0.520        0.544
  120    484     447    0.372 0.00817        0.357        0.389

                TNM_PATH_STAGE_GROUP=3C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5036    1266    0.803 0.00496        0.793        0.813
   24   3296    1284    0.589 0.00630        0.577        0.601
   36   2166     656    0.462 0.00662        0.450        0.476
   48   1438     319    0.387 0.00677        0.373        0.400
   60   1014     124    0.349 0.00690        0.336        0.363
  120    166     162    0.253 0.00876        0.237        0.271

                TNM_PATH_STAGE_GROUP=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2857    4448   0.4024 0.00572        0.391        0.414
   24   1592    1019   0.2529 0.00518        0.243        0.263
   36    969     385   0.1862 0.00481        0.177        0.196
   48    673     155   0.1539 0.00463        0.145        0.163
   60    489      66   0.1375 0.00456        0.129        0.147
  120    100     104   0.0952 0.00496        0.086        0.105

                TNM_PATH_STAGE_GROUP=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1406     113    0.928 0.00655        0.915        0.941
   24   1275      98    0.862 0.00882        0.845        0.880
   36   1171      67    0.816 0.00998        0.797        0.836
   48   1074      61    0.773 0.01088        0.752        0.795
   60    987      40    0.744 0.01142        0.722        0.766
  120    400     138    0.619 0.01375        0.593        0.647

                TNM_PATH_STAGE_GROUP=99 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  73796    8329    0.904 0.00100        0.902        0.906
   24  63760    4646    0.845 0.00126        0.842        0.847
   36  54124    3300    0.799 0.00142        0.796        0.801
   48  45892    2533    0.759 0.00156        0.756        0.762
   60  38530    1890    0.726 0.00167        0.722        0.729
  120  10063    4840    0.590 0.00232        0.586        0.595




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_STAGE_GROUP
X matrix deemed to be singular; variable 4 14 15 16
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_STAGE_GROUP, data = data)

  n= 351764, number of events= 86848 
   (11146 observations deleted due to missingness)

                            coef exp(coef) se(coef)      z Pr(>|z|)    
TNM_PATH_STAGE_GROUP1   -0.27207   0.76180  0.05514 -4.934 8.04e-07 ***
TNM_PATH_STAGE_GROUP1A  -0.46113   0.63057  0.04836 -9.536  < 2e-16 ***
TNM_PATH_STAGE_GROUP1B  -0.15970   0.85240  0.04833 -3.304 0.000953 ***
TNM_PATH_STAGE_GROUP1C        NA        NA  0.00000     NA       NA    
TNM_PATH_STAGE_GROUP2    0.46330   1.58930  0.06457  7.175 7.22e-13 ***
TNM_PATH_STAGE_GROUP2A   0.45204   1.57152  0.04896  9.233  < 2e-16 ***
TNM_PATH_STAGE_GROUP2B   0.87922   2.40902  0.04903 17.931  < 2e-16 ***
TNM_PATH_STAGE_GROUP2C   1.39818   4.04783  0.04975 28.103  < 2e-16 ***
TNM_PATH_STAGE_GROUP3    1.00907   2.74306  0.05062 19.934  < 2e-16 ***
TNM_PATH_STAGE_GROUP3A   0.50918   1.66393  0.05098  9.988  < 2e-16 ***
TNM_PATH_STAGE_GROUP3B   1.10511   3.01956  0.04996 22.119  < 2e-16 ***
TNM_PATH_STAGE_GROUP3C   1.69328   5.43730  0.05005 33.831  < 2e-16 ***
TNM_PATH_STAGE_GROUP4    2.69749  14.84243  0.04907 54.971  < 2e-16 ***
TNM_PATH_STAGE_GROUP4A        NA        NA  0.00000     NA       NA    
TNM_PATH_STAGE_GROUP4B        NA        NA  0.00000     NA       NA    
TNM_PATH_STAGE_GROUP4C        NA        NA  0.00000     NA       NA    
TNM_PATH_STAGE_GROUPN_A  0.36782   1.44458  0.06379  5.766 8.10e-09 ***
TNM_PATH_STAGE_GROUP99   0.49614   1.64238  0.04775 10.389  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_STAGE_GROUP1      0.7618    1.31268    0.6838    0.8487
TNM_PATH_STAGE_GROUP1A     0.6306    1.58587    0.5736    0.6933
TNM_PATH_STAGE_GROUP1B     0.8524    1.17316    0.7754    0.9371
TNM_PATH_STAGE_GROUP1C         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP2      1.5893    0.62921    1.4004    1.8037
TNM_PATH_STAGE_GROUP2A     1.5715    0.63633    1.4277    1.7298
TNM_PATH_STAGE_GROUP2B     2.4090    0.41511    2.1883    2.6520
TNM_PATH_STAGE_GROUP2C     4.0478    0.24705    3.6717    4.4624
TNM_PATH_STAGE_GROUP3      2.7431    0.36456    2.4840    3.0292
TNM_PATH_STAGE_GROUP3A     1.6639    0.60099    1.5057    1.8388
TNM_PATH_STAGE_GROUP3B     3.0196    0.33117    2.7379    3.3302
TNM_PATH_STAGE_GROUP3C     5.4373    0.18391    4.9292    5.9977
TNM_PATH_STAGE_GROUP4     14.8424    0.06737   13.4814   16.3408
TNM_PATH_STAGE_GROUP4A         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP4B         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP4C         NA         NA        NA        NA
TNM_PATH_STAGE_GROUPN_A    1.4446    0.69224    1.2748    1.6370
TNM_PATH_STAGE_GROUP99     1.6424    0.60887    1.4956    1.8035

Concordance= 0.698  (se = 0.001 )
Rsquare= 0.116   (max possible= 0.998 )
Likelihood ratio test= 43484  on 14 df,   p=0
Wald test            = 54341  on 14 df,   p=0
Score (logrank) test = 78834  on 14 df,   p=0
Removed 5 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_STAGE_GROUP
This manual palette can handle a maximum of 10 values. You have supplied 15.

Margins

uni_var(test_var = "MARGINS", data_imp = data)
_________________________________________________
   
## MARGINS
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ MARGINS, data = data)

                               n events median 0.95LCL 0.95UCL
MARGINS=No Residual       329265  70369     NA  164.73      NA
MARGINS=Residual, NOS       5326   2401  74.12   68.83    80.5
MARGINS=Microscopic Resid   5723   2641  68.11   64.89    73.4
MARGINS=Macroscopic Resid    366    242  23.06   19.25    27.8
MARGINS=Not evaluable        999    433  82.40   73.66   104.2
MARGINS=No surg            16733  12252   8.87    8.57     9.2
MARGINS=Unknown             4498   1634 139.24  129.31   149.4

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ MARGINS, data = data)

                MARGINS=No Residual 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 284582   10282    0.967 0.000324        0.966        0.967
   24 246304   14276    0.916 0.000514        0.915        0.917
   36 204546   11986    0.868 0.000646        0.867        0.870
   48 168935    8929    0.828 0.000746        0.826        0.829
   60 136867    6838    0.791 0.000833        0.790        0.793
  120  32872   15998    0.646 0.001316        0.643        0.648

                MARGINS=Residual, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3980     929    0.818 0.00541        0.807        0.829
   24   3200     494    0.713 0.00647        0.700        0.725
   36   2576     316    0.638 0.00702        0.625        0.652
   48   2032     241    0.575 0.00742        0.561        0.590
   60   1659     114    0.541 0.00764        0.526        0.556
  120    346     280    0.399 0.00973        0.381        0.419

                MARGINS=Microscopic Resid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4455     863    0.843 0.00492        0.833        0.853
   24   3552     597    0.726 0.00615        0.714        0.738
   36   2817     383    0.643 0.00674        0.630        0.657
   48   2240     268    0.579 0.00713        0.565        0.593
   60   1808     168    0.532 0.00740        0.518        0.547
  120    334     324    0.374 0.00983        0.355        0.394

                MARGINS=Macroscopic Resid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    229     120    0.663  0.0251        0.615        0.714
   24    149      60    0.481  0.0271        0.431        0.538
   36    107      25    0.397  0.0271        0.348        0.454
   48     79      17    0.331  0.0270        0.282        0.388
   60     63       3    0.317  0.0270        0.268        0.375
  120      9      14    0.192  0.0330        0.137        0.269

                MARGINS=Not evaluable 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    764     162    0.830  0.0122        0.806        0.854
   24    603     107    0.709  0.0150        0.681        0.739
   36    499      51    0.646  0.0161        0.616        0.679
   48    416      25    0.612  0.0166        0.580        0.646
   60    332      26    0.570  0.0174        0.537        0.605
  120     82      57    0.430  0.0216        0.390        0.475

                MARGINS=No surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   6526    9055    0.435 0.00394        0.428        0.443
   24   4167    1743    0.314 0.00377        0.306        0.321
   36   2879     674    0.259 0.00366        0.252        0.266
   48   2112     329    0.226 0.00361        0.219        0.234
   60   1593     168    0.207 0.00361        0.200        0.214
  120    340     255    0.157 0.00402        0.149        0.165

                MARGINS=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3621     536    0.875 0.00504        0.866        0.885
   24   3084     346    0.789 0.00632        0.777        0.802
   36   2655     216    0.732 0.00697        0.718        0.746
   48   2320     140    0.692 0.00737        0.677        0.706
   60   1980      93    0.662 0.00767        0.647        0.677
  120    589     261    0.536 0.00970        0.517        0.555




   
## Univariable Cox Proportional Hazard Model for:  MARGINS

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ MARGINS, data = data)

  n= 362910, number of events= 89972 

                             coef exp(coef) se(coef)      z Pr(>|z|)    
MARGINSResidual, NOS     0.967379  2.631040 0.020756  46.61   <2e-16 ***
MARGINSMicroscopic Resid 0.985189  2.678317 0.019826  49.69   <2e-16 ***
MARGINSMacroscopic Resid 1.732810  5.656527 0.064404  26.91   <2e-16 ***
MARGINSNot evaluable     0.873543  2.395383 0.048205  18.12   <2e-16 ***
MARGINSNo surg           2.251671  9.503601 0.009911 227.19   <2e-16 ***
MARGINSUnknown           0.520778  1.683337 0.025027  20.81   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                         exp(coef) exp(-coef) lower .95 upper .95
MARGINSResidual, NOS         2.631     0.3801     2.526     2.740
MARGINSMicroscopic Resid     2.678     0.3734     2.576     2.784
MARGINSMacroscopic Resid     5.657     0.1768     4.986     6.418
MARGINSNot evaluable         2.395     0.4175     2.179     2.633
MARGINSNo surg               9.504     0.1052     9.321     9.690
MARGINSUnknown               1.683     0.5941     1.603     1.768

Concordance= 0.612  (se = 0 )
Rsquare= 0.091   (max possible= 0.998 )
Likelihood ratio test= 34649  on 6 df,   p=0
Wald test            = 54125  on 6 df,   p=0
Score (logrank) test = 78275  on 6 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  MARGINS

Margins Yes/No

#uni_var(test_var = "MARGINS_YN", data_imp = data)

30 Day Readmission

uni_var(test_var = "READM_HOSP_30_DAYS_F", data_imp = data)
_________________________________________________
   
## READM_HOSP_30_DAYS_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ READM_HOSP_30_DAYS_F, data = data)

                                                n events median 0.95LCL 0.95UCL
READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit 347736  84921    164     162     165
READM_HOSP_30_DAYS_F=Unplan_Readmit_Same     3407   1253    113     103     124
READM_HOSP_30_DAYS_F=Plan_Readmit_Same       5388   1646    157     140      NA
READM_HOSP_30_DAYS_F=PlanUnplan_Same          515    135    130     122      NA
READM_HOSP_30_DAYS_F=9                       5864   2017    160     158      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ READM_HOSP_30_DAYS_F, data = data)

                READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 291361   20768    0.937 0.000424        0.936        0.938
   24 249996   16607    0.881 0.000579        0.880        0.882
   36 206568   12940    0.833 0.000687        0.831        0.834
   48 169989    9385    0.792 0.000771        0.790        0.794
   60 137365    7017    0.757 0.000845        0.755        0.758
  120  32483   16133    0.616 0.001285        0.613        0.618

                READM_HOSP_30_DAYS_F=Unplan_Readmit_Same 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2713     385    0.882 0.00567        0.871        0.893
   24   2245     264    0.793 0.00728        0.779        0.807
   36   1869     156    0.735 0.00810        0.719        0.751
   48   1536     133    0.679 0.00880        0.662        0.697
   60   1236      87    0.638 0.00932        0.620        0.657
  120    303     202    0.484 0.01245        0.460        0.509

                READM_HOSP_30_DAYS_F=Plan_Readmit_Same 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4635     280    0.945 0.00320        0.939        0.951
   24   3988     361    0.869 0.00482        0.860        0.879
   36   3370     264    0.809 0.00574        0.798        0.820
   48   2819     195    0.760 0.00639        0.747        0.772
   60   2327     148    0.717 0.00693        0.703        0.731
  120    702     348    0.566 0.00934        0.548        0.585

                READM_HOSP_30_DAYS_F=PlanUnplan_Same 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    450      30    0.939  0.0107        0.918        0.961
   24    399      20    0.896  0.0139        0.869        0.924
   36    315      24    0.837  0.0175        0.803        0.872
   48    261      20    0.781  0.0203        0.742        0.822
   60    217      10    0.750  0.0217        0.709        0.794
  120     24      29    0.573  0.0388        0.502        0.654

                READM_HOSP_30_DAYS_F=9 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4998     484    0.914 0.00373        0.907        0.922
   24   4431     371    0.845 0.00488        0.836        0.855
   36   3957     267    0.793 0.00553        0.782        0.804
   48   3529     216    0.748 0.00599        0.737        0.760
   60   3157     148    0.716 0.00629        0.704        0.728
  120   1060     477    0.578 0.00779        0.563        0.593




   
## Univariable Cox Proportional Hazard Model for:  READM_HOSP_30_DAYS_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ READM_HOSP_30_DAYS_F, data = data)

  n= 362910, number of events= 89972 

                                           coef exp(coef) se(coef)      z Pr(>|z|)    
READM_HOSP_30_DAYS_FUnplan_Readmit_Same 0.47735   1.61180  0.02846 16.774  < 2e-16 ***
READM_HOSP_30_DAYS_FPlan_Readmit_Same   0.14940   1.16114  0.02489  6.003 1.94e-09 ***
READM_HOSP_30_DAYS_FPlanUnplan_Same     0.07259   1.07529  0.08614  0.843    0.399    
READM_HOSP_30_DAYS_F9                   0.14804   1.15956  0.02254  6.567 5.13e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                        exp(coef) exp(-coef) lower .95 upper .95
READM_HOSP_30_DAYS_FUnplan_Readmit_Same     1.612     0.6204    1.5244     1.704
READM_HOSP_30_DAYS_FPlan_Readmit_Same       1.161     0.8612    1.1059     1.219
READM_HOSP_30_DAYS_FPlanUnplan_Same         1.075     0.9300    0.9083     1.273
READM_HOSP_30_DAYS_F9                       1.160     0.8624    1.1094     1.212

Concordance= 0.506  (se = 0 )
Rsquare= 0.001   (max possible= 0.998 )
Likelihood ratio test= 311.7  on 4 df,   p=0
Wald test            = 352.3  on 4 df,   p=0
Score (logrank) test = 357.7  on 4 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  READM_HOSP_30_DAYS_F

Radiation Type

uni_var(test_var = "RX_SUMM_RADIATION_F", data_imp = data)
_________________________________________________
   
## RX_SUMM_RADIATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_SUMM_RADIATION_F, data = data)

                                                n events median 0.95LCL 0.95UCL
RX_SUMM_RADIATION_F=None                   349080  81074  164.7   164.5      NA
RX_SUMM_RADIATION_F=Beam Radiation          11458   8190   15.5    14.9    16.2
RX_SUMM_RADIATION_F=Radioactive Implants       44     20   64.3    33.7      NA
RX_SUMM_RADIATION_F=Radioisotopes               9      5   95.3    39.0      NA
RX_SUMM_RADIATION_F=Beam + Imp or Isotopes      9      6   24.0    16.1      NA
RX_SUMM_RADIATION_F=Radiation, NOS            114     79   25.7    15.6    35.7
RX_SUMM_RADIATION_F=Unknown                  2196    598     NA   154.8      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_SUMM_RADIATION_F, data = data)

                RX_SUMM_RADIATION_F=None 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 296059   16779    0.949 0.000383        0.948        0.950
   24 255224   15967    0.896 0.000547        0.895        0.897
   36 211644   12838    0.848 0.000662        0.846        0.849
   48 174574    9535    0.807 0.000752        0.805        0.808
   60 141423    7141    0.771 0.000829        0.769        0.773
  120  33869   16672    0.628 0.001276        0.625        0.630

                RX_SUMM_RADIATION_F=Beam Radiation 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   6075    5005    0.557 0.00469        0.548        0.566
   24   4029    1546    0.409 0.00472        0.400        0.419
   36   2806     733    0.330 0.00463        0.321        0.339
   48   2054     359    0.284 0.00458        0.275        0.293
   60   1507     208    0.253 0.00456        0.244        0.262
  120    253     312    0.167 0.00542        0.157        0.178

                RX_SUMM_RADIATION_F=Radioactive Implants 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     36       6    0.861  0.0525        0.764        0.971
   24     24       8    0.654  0.0755        0.522        0.820
   36     17       2    0.589  0.0809        0.450        0.770
   48     13       0    0.589  0.0809        0.450        0.770
   60     11       1    0.543  0.0864        0.398        0.742
  120      2       3    0.264  0.1302        0.100        0.694

                RX_SUMM_RADIATION_F=Radioisotopes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      7       1    0.889   0.105        0.706            1
   24      6       1    0.762   0.148        0.521            1
   36      6       0    0.762   0.148        0.521            1
   48      4       2    0.508   0.177        0.257            1
   60      4       0    0.508   0.177        0.257            1

                RX_SUMM_RADIATION_F=Beam + Imp or Isotopes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      7       2    0.778   0.139        0.549        1.000
   24      5       2    0.556   0.166        0.310        0.997
   36      3       2    0.333   0.157        0.132        0.840
   48      3       0    0.333   0.157        0.132        0.840
   60      2       0    0.333   0.157        0.132        0.840

                RX_SUMM_RADIATION_F=Radiation, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     69      39    0.647  0.0456        0.564        0.743
   24     50      14    0.513  0.0482        0.427        0.617
   36     32      12    0.384  0.0485        0.300        0.491
   48     25       4    0.332  0.0483        0.250        0.442
   60     20       4    0.278  0.0474        0.199        0.389
  120      6       3    0.208  0.0521        0.127        0.340

                RX_SUMM_RADIATION_F=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1904     115    0.945 0.00501        0.935        0.955
   24   1721      85    0.902 0.00662        0.889        0.915
   36   1571      64    0.867 0.00764        0.852        0.882
   48   1461      49    0.839 0.00835        0.823        0.856
   60   1335      56    0.807 0.00911        0.789        0.825
  120    442     198    0.639 0.01323        0.613        0.665




   
## Univariable Cox Proportional Hazard Model for:  RX_SUMM_RADIATION_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_SUMM_RADIATION_F, data = data)

  n= 362910, number of events= 89972 

                                              coef exp(coef) se(coef)       z Pr(>|z|)    
RX_SUMM_RADIATION_FBeam Radiation          1.83534   6.26725  0.01167 157.210  < 2e-16 ***
RX_SUMM_RADIATION_FRadioactive Implants    0.98420   2.67568  0.22364   4.401 1.08e-05 ***
RX_SUMM_RADIATION_FRadioisotopes           0.88426   2.42118  0.44723   1.977 0.048020 *  
RX_SUMM_RADIATION_FBeam + Imp or Isotopes  1.38614   3.99938  0.40826   3.395 0.000686 ***
RX_SUMM_RADIATION_FRadiation, NOS          1.60332   4.96951  0.11257  14.243  < 2e-16 ***
RX_SUMM_RADIATION_FUnknown                -0.08043   0.92272  0.04105  -1.959 0.050089 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                          exp(coef) exp(-coef) lower .95 upper .95
RX_SUMM_RADIATION_FBeam Radiation            6.2673     0.1596    6.1255     6.412
RX_SUMM_RADIATION_FRadioactive Implants      2.6757     0.3737    1.7261     4.148
RX_SUMM_RADIATION_FRadioisotopes             2.4212     0.4130    1.0077     5.817
RX_SUMM_RADIATION_FBeam + Imp or Isotopes    3.9994     0.2500    1.7967     8.902
RX_SUMM_RADIATION_FRadiation, NOS            4.9695     0.2012    3.9856     6.196
RX_SUMM_RADIATION_FUnknown                   0.9227     1.0838    0.8514     1.000

Concordance= 0.552  (se = 0 )
Rsquare= 0.043   (max possible= 0.998 )
Likelihood ratio test= 15804  on 6 df,   p=0
Wald test            = 24931  on 6 df,   p=0
Score (logrank) test = 32711  on 6 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RX_SUMM_RADIATION_F

Lymphovascular Invasion

uni_var(test_var = "LYMPH_VASCULAR_INVASION_F", data_imp = data)
_________________________________________________
   
## LYMPH_VASCULAR_INVASION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ LYMPH_VASCULAR_INVASION_F, data = data)

   159900 observations deleted due to missingness 
                                                 n events median 0.95LCL 0.95UCL
LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv 145065  21475  95.93   95.05      NA
LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv   7694   3337  48.66   46.06    51.9
LYMPH_VASCULAR_INVASION_F=N_A                   50     30   8.87    6.87      NA
LYMPH_VASCULAR_INVASION_F=Unknown            50201  13034  94.62   94.03      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ LYMPH_VASCULAR_INVASION_F, data = data)

159900 observations deleted due to missingness 
                LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 120432    4744    0.964 0.000508        0.963        0.965
   24  97371    5627    0.916 0.000793        0.915        0.918
   36  70251    4462    0.868 0.001026        0.866        0.870
   48  47908    3006    0.825 0.001246        0.822        0.827
   60  29223    1942    0.784 0.001498        0.781        0.787

                LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5771    1247    0.830 0.00440        0.821        0.838
   24   4077    1019    0.674 0.00567        0.663        0.686
   36   2690     553    0.573 0.00626        0.561        0.585
   48   1720     284    0.503 0.00675        0.490        0.516
   60    969     134    0.455 0.00730        0.441        0.469

                LYMPH_VASCULAR_INVASION_F=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     18      25    0.471  0.0734       0.3470        0.639
   24      7       3    0.369  0.0783       0.2434        0.559
   36      1       2    0.197  0.1007       0.0722        0.536

                LYMPH_VASCULAR_INVASION_F=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  37970    6232    0.869 0.00155        0.866        0.872
   24  30826    2671    0.804 0.00188        0.800        0.808
   36  23552    1715    0.755 0.00211        0.750        0.759
   48  17056    1098    0.715 0.00232        0.710        0.719
   60  10962     668    0.681 0.00255        0.676        0.686




   
## Univariable Cox Proportional Hazard Model for:  LYMPH_VASCULAR_INVASION_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ LYMPH_VASCULAR_INVASION_F, data = data)

  n= 203010, number of events= 37876 
   (159900 observations deleted due to missingness)

                                               coef exp(coef) se(coef)     z Pr(>|z|)    
LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv  1.26944   3.55885  0.01862 68.17   <2e-16 ***
LYMPH_VASCULAR_INVASION_FN_A                2.54386  12.72866  0.18275 13.92   <2e-16 ***
LYMPH_VASCULAR_INVASION_FUnknown            0.60603   1.83314  0.01111 54.56   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                           exp(coef) exp(-coef) lower .95 upper .95
LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv     3.559    0.28099     3.431     3.691
LYMPH_VASCULAR_INVASION_FN_A                  12.729    0.07856     8.897    18.211
LYMPH_VASCULAR_INVASION_FUnknown               1.833    0.54551     1.794     1.873

Concordance= 0.609  (se = 0.001 )
Rsquare= 0.027   (max possible= 0.987 )
Likelihood ratio test= 5553  on 3 df,   p=0
Wald test            = 6417  on 3 df,   p=0
Score (logrank) test = 7039  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  LYMPH_VASCULAR_INVASION_F

Endoscopic/Robotic

uni_var(test_var = "RX_HOSP_SURG_APPR_2010_F", data_imp = data)
_________________________________________________
   
## RX_HOSP_SURG_APPR_2010_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)

   159900 observations deleted due to missingness 
                                               n events median 0.95LCL 0.95UCL
RX_HOSP_SURG_APPR_2010_F=No_Surg           18689   8965   36.0   33.97    37.9
RX_HOSP_SURG_APPR_2010_F=Robot_Assist        148     26     NA      NA      NA
RX_HOSP_SURG_APPR_2010_F=Robot_to_Open        37      5     NA   56.71      NA
RX_HOSP_SURG_APPR_2010_F=Endo_Lap            441    109     NA   85.29      NA
RX_HOSP_SURG_APPR_2010_F=Endo_Lap_to_Open    170     31     NA   81.51      NA
RX_HOSP_SURG_APPR_2010_F=Open_Unknown     183492  28727   95.5   95.05      NA
RX_HOSP_SURG_APPR_2010_F=Unknown              33     13   14.8    6.24      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)

159900 observations deleted due to missingness 
                RX_HOSP_SURG_APPR_2010_F=No_Surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  10832    5927    0.665 0.00356        0.659        0.672
   24   7778    1575    0.562 0.00385        0.555        0.570
   36   5216     764    0.500 0.00404        0.492        0.508
   48   3429     385    0.457 0.00425        0.449        0.465
   60   2057     170    0.429 0.00452        0.420        0.438

                RX_HOSP_SURG_APPR_2010_F=Robot_Assist 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    121       8    0.941  0.0204        0.902        0.981
   24     99       6    0.890  0.0280        0.836        0.946
   36     74       6    0.828  0.0357        0.761        0.901
   48     48       3    0.791  0.0400        0.716        0.873
   60     24       1    0.766  0.0458        0.681        0.861

                RX_HOSP_SURG_APPR_2010_F=Robot_to_Open 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     28       3    0.908  0.0508        0.814            1
   24     21       0    0.908  0.0508        0.814            1
   36     17       0    0.908  0.0508        0.814            1
   48     12       0    0.908  0.0508        0.814            1
   60      2       2    0.692  0.1398        0.465            1

                RX_HOSP_SURG_APPR_2010_F=Endo_Lap 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    342      38    0.906  0.0145        0.878        0.935
   24    269      24    0.837  0.0190        0.801        0.876
   36    189      24    0.753  0.0237        0.708        0.801
   48    122      14    0.687  0.0275        0.635        0.743
   60     66       8    0.636  0.0308        0.579        0.700

                RX_HOSP_SURG_APPR_2010_F=Endo_Lap_to_Open 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    134       7    0.954  0.0169        0.922        0.988
   24    119       4    0.924  0.0221        0.882        0.968
   36    101       6    0.874  0.0289        0.819        0.932
   48     73       3    0.845  0.0324        0.784        0.911
   60     54       1    0.833  0.0341        0.769        0.903

                RX_HOSP_SURG_APPR_2010_F=Open_Unknown 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 152722    6256    0.963 0.000460        0.962        0.964
   24 123988    7707    0.911 0.000723        0.910        0.912
   36  90891    5932    0.862 0.000925        0.860        0.864
   48  62997    3983    0.818 0.001110        0.816        0.820
   60  38949    2562    0.777 0.001317        0.775        0.780

                RX_HOSP_SURG_APPR_2010_F=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     12       9    0.601   0.105        0.427        0.846
   24      7       4    0.401   0.108        0.237        0.679
   36      6       0    0.401   0.108        0.237        0.679
   48      3       0    0.401   0.108        0.237        0.679
   60      2       0    0.401   0.108        0.237        0.679




   
## Univariable Cox Proportional Hazard Model for:  RX_HOSP_SURG_APPR_2010_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)

  n= 203010, number of events= 37876 
   (159900 observations deleted due to missingness)

                                             coef exp(coef) se(coef)        z Pr(>|z|)    
RX_HOSP_SURG_APPR_2010_FRobot_Assist     -1.40546   0.24526  0.19640   -7.156 8.31e-13 ***
RX_HOSP_SURG_APPR_2010_FRobot_to_Open    -1.52765   0.21705  0.44734   -3.415 0.000638 ***
RX_HOSP_SURG_APPR_2010_FEndo_Lap         -0.96163   0.38227  0.09637   -9.979  < 2e-16 ***
RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open -1.49545   0.22415  0.17993   -8.312  < 2e-16 ***
RX_HOSP_SURG_APPR_2010_FOpen_Unknown     -1.53853   0.21470  0.01213 -126.816  < 2e-16 ***
RX_HOSP_SURG_APPR_2010_FUnknown           0.33426   1.39691  0.27755    1.204 0.228472    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                         exp(coef) exp(-coef) lower .95 upper .95
RX_HOSP_SURG_APPR_2010_FRobot_Assist        0.2453     4.0774   0.16689    0.3604
RX_HOSP_SURG_APPR_2010_FRobot_to_Open       0.2170     4.6073   0.09032    0.5216
RX_HOSP_SURG_APPR_2010_FEndo_Lap            0.3823     2.6160   0.31648    0.4617
RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open    0.2241     4.4614   0.15754    0.3189
RX_HOSP_SURG_APPR_2010_FOpen_Unknown        0.2147     4.6577   0.20965    0.2199
RX_HOSP_SURG_APPR_2010_FUnknown             1.3969     0.7159   0.81080    2.4067

Concordance= 0.617  (se = 0.001 )
Rsquare= 0.057   (max possible= 0.987 )
Likelihood ratio test= 11999  on 6 df,   p=0
Wald test            = 16121  on 6 df,   p=0
Score (logrank) test = 19537  on 6 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RX_HOSP_SURG_APPR_2010_F

Surgery Radiation Sequence

uni_var(test_var = "SURG_RAD_SEQ", data_imp = data)
_________________________________________________
   
## SURG_RAD_SEQ
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ, data = data)

                                            n events median 0.95LCL 0.95UCL
SURG_RAD_SEQ=Surg Alone                337579  73170     NA  164.60      NA
SURG_RAD_SEQ=Surg then Rad               6249   3820  32.16   30.78   33.77
SURG_RAD_SEQ=Rad Alone                   5240   4377   6.14    5.88    6.51
SURG_RAD_SEQ=No Treatment               10837   7403  11.43   10.97   12.00
SURG_RAD_SEQ=Other                       2891   1115 132.83  120.51  150.70
SURG_RAD_SEQ=Rad before and after Surg     17     14   9.07    4.01   24.11
SURG_RAD_SEQ=Rad then Surg                 97     73  15.21   10.87   23.49

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ, data = data)

                SURG_RAD_SEQ=Surg Alone 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 291152   11156    0.965 0.000328        0.964        0.965
   24 251965   14780    0.914 0.000514        0.913        0.915
   36 209336   12350    0.866 0.000642        0.865        0.867
   48 172875    9282    0.825 0.000741        0.823        0.826
   60 140127    7012    0.788 0.000826        0.787        0.790
  120  33580   16468    0.642 0.001299        0.640        0.645

                SURG_RAD_SEQ=Surg then Rad 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4522    1515    0.753 0.00550        0.743        0.764
   24   3177     999    0.581 0.00642        0.568        0.593
   36   2276     561    0.472 0.00666        0.459        0.485
   48   1677     287    0.408 0.00675        0.395        0.421
   60   1238     174    0.362 0.00684        0.348        0.375
  120    209     262    0.237 0.00844        0.221        0.254

                SURG_RAD_SEQ=Rad Alone 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1594    3476   0.3258 0.00657       0.3132        0.339
   24    886     551   0.2074 0.00582       0.1963        0.219
   36    552     180   0.1609 0.00546       0.1505        0.172
   48    392      76   0.1369 0.00530       0.1269        0.148
   60    280      38   0.1225 0.00524       0.1126        0.133
  120     45      48   0.0874 0.00613       0.0762        0.100

                SURG_RAD_SEQ=No Treatment 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4680    5246    0.490 0.00496        0.480        0.500
   24   3105    1132    0.366 0.00490        0.356        0.376
   36   2195     459    0.308 0.00482        0.298        0.317
   48   1608     241    0.271 0.00480        0.261        0.280
   60   1225     115    0.249 0.00482        0.240        0.259
  120    272     191    0.191 0.00542        0.181        0.202

                SURG_RAD_SEQ=Other 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2148     502    0.818 0.00736        0.804        0.832
   24   1888     141    0.763 0.00820        0.747        0.779
   36   1694      93    0.724 0.00871        0.707        0.741
   48   1559      63    0.697 0.00904        0.679        0.715
   60   1413      70    0.665 0.00940        0.646        0.683
  120    459     214    0.524 0.01160        0.502        0.547

                SURG_RAD_SEQ=Rad before and after Surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6      11    0.353  0.1159       0.1854        0.672
   24      4       2    0.235  0.1029       0.0999        0.554
   36      3       1    0.176  0.0925       0.0632        0.493
   48      3       0    0.176  0.0925       0.0632        0.493
   60      3       0    0.176  0.0925       0.0632        0.493
  120      3       0    0.176  0.0925       0.0632        0.493

                SURG_RAD_SEQ=Rad then Surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     55      41    0.571  0.0507       0.4798        0.680
   24     34      18    0.378  0.0500       0.2914        0.490
   36     23       7    0.295  0.0480       0.2148        0.406
   48     20       0    0.295  0.0480       0.2148        0.406
   60     16       1    0.279  0.0480       0.1991        0.391
  120      4       6    0.109  0.0508       0.0439        0.272




   
## Univariable Cox Proportional Hazard Model for:  SURG_RAD_SEQ

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ, data = data)

  n= 362910, number of events= 89972 

                                          coef exp(coef) se(coef)       z Pr(>|z|)    
SURG_RAD_SEQSurg then Rad              1.46805   4.34076  0.01663  88.286  < 2e-16 ***
SURG_RAD_SEQRad Alone                  2.66145  14.31699  0.01575 168.937  < 2e-16 ***
SURG_RAD_SEQNo Treatment               2.06172   7.85949  0.01226 168.104  < 2e-16 ***
SURG_RAD_SEQOther                      0.54046   1.71679  0.03018  17.905  < 2e-16 ***
SURG_RAD_SEQRad before and after Surg  2.01488   7.49982  0.26729   7.538 4.77e-14 ***
SURG_RAD_SEQRad then Surg              1.93211   6.90408  0.11711  16.499  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                      exp(coef) exp(-coef) lower .95 upper .95
SURG_RAD_SEQSurg then Rad                 4.341    0.23037     4.202     4.485
SURG_RAD_SEQRad Alone                    14.317    0.06985    13.882    14.766
SURG_RAD_SEQNo Treatment                  7.859    0.12723     7.673     8.051
SURG_RAD_SEQOther                         1.717    0.58248     1.618     1.821
SURG_RAD_SEQRad before and after Surg     7.500    0.13334     4.441    12.664
SURG_RAD_SEQRad then Surg                 6.904    0.14484     5.488     8.685

Concordance= 0.605  (se = 0 )
Rsquare= 0.094   (max possible= 0.998 )
Likelihood ratio test= 35691  on 6 df,   p=0
Wald test            = 57107  on 6 df,   p=0
Score (logrank) test = 86266  on 6 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SURG_RAD_SEQ

Surgery Yes/No

uni_var(test_var = "SURGERY_YN", data_imp = data)
_________________________________________________
   
## SURGERY_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURGERY_YN, data = data)

                    n events median 0.95LCL 0.95UCL
SURGERY_YN=No   16214  11864   8.77    8.48     9.1
SURGERY_YN=Ukn    684    462  14.19   11.83    16.4
SURGERY_YN=Yes 346012  77646 165.39  164.57      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURGERY_YN, data = data)

                SURGERY_YN=No 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   6305    8805    0.434 0.00400        0.426        0.442
   24   4021    1679    0.313 0.00383        0.305        0.320
   36   2771     641    0.259 0.00372        0.251        0.266
   48   2025     316    0.226 0.00367        0.219        0.234
   60   1528     153    0.208 0.00367        0.201        0.215
  120    323     243    0.157 0.00413        0.149        0.166

                SURGERY_YN=Ukn 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    310     291    0.533  0.0201        0.495        0.574
   24    205      79    0.392  0.0201        0.355        0.433
   36    148      37    0.316  0.0197        0.280        0.357
   48    120      15    0.282  0.0195        0.247        0.323
   60     93      18    0.238  0.0190        0.204        0.279
  120     22      21    0.172  0.0186        0.139        0.212

                SURGERY_YN=Yes 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 297542   12851    0.960 0.000342        0.960        0.961
   24 256833   15865    0.907 0.000524        0.906        0.908
   36 213160   12973    0.858 0.000648        0.857        0.859
   48 175989    9618    0.817 0.000743        0.815        0.818
   60 142681    7239    0.780 0.000824        0.779        0.782
  120  34227   16925    0.635 0.001283        0.632        0.637




   
## Univariable Cox Proportional Hazard Model for:  SURGERY_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURGERY_YN, data = data)

  n= 362910, number of events= 89972 

                  coef exp(coef) se(coef)        z Pr(>|z|)    
SURGERY_YNUkn -0.27473   0.75977  0.04742   -5.793 6.91e-09 ***
SURGERY_YNYes -2.19889   0.11093  0.00998 -220.333  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
SURGERY_YNUkn    0.7598      1.316    0.6923    0.8338
SURGERY_YNYes    0.1109      9.015    0.1088    0.1131

Concordance= 0.585  (se = 0 )
Rsquare= 0.08   (max possible= 0.998 )
Likelihood ratio test= 30337  on 2 df,   p=0
Wald test            = 49726  on 2 df,   p=0
Score (logrank) test = 72791  on 2 df,   p=0
no non-missing arguments to min; returning Infno non-missing arguments to max; returning -InfTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 1 rows containing missing values (geom_errorbar).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SURGERY_YN

Radiation Yes/No

uni_var(test_var = "RADIATION_YN", data_imp = data)
_________________________________________________
   
## RADIATION_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RADIATION_YN, data = data)

   2370 observations deleted due to missingness 
                      n events median 0.95LCL 0.95UCL
RADIATION_YN=No  348906  80900  164.7   164.5      NA
RADIATION_YN=Yes  11634   8300   15.7    15.1    16.4

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RADIATION_YN, data = data)

2370 observations deleted due to missingness 
                RADIATION_YN=No 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 296056   16607    0.950 0.000381        0.949        0.950
   24 255222   15967    0.896 0.000546        0.895        0.897
   36 211643   12837    0.848 0.000662        0.847        0.849
   48 174573    9535    0.807 0.000752        0.806        0.809
   60 141422    7141    0.771 0.000829        0.770        0.773
  120  33869   16671    0.628 0.001277        0.626        0.631

                RADIATION_YN=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   6194    5053    0.559 0.00465        0.550        0.569
   24   4114    1571    0.412 0.00469        0.402        0.421
   36   2864     749    0.331 0.00460        0.323        0.341
   48   2099     365    0.286 0.00455        0.277        0.295
   60   1544     213    0.254 0.00454        0.246        0.263
  120    261     319    0.168 0.00540        0.158        0.179




   
## Univariable Cox Proportional Hazard Model for:  RADIATION_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RADIATION_YN, data = data)

  n= 360540, number of events= 89200 
   (2370 observations deleted due to missingness)

                   coef exp(coef) se(coef)     z Pr(>|z|)    
RADIATION_YNYes 1.83188   6.24562  0.01161 157.9   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
RADIATION_YNYes     6.246     0.1601     6.105     6.389

Concordance= 0.552  (se = 0 )
Rsquare= 0.043   (max possible= 0.998 )
Likelihood ratio test= 15806  on 1 df,   p=0
Wald test            = 24917  on 1 df,   p=0
Score (logrank) test = 32677  on 1 df,   p=0





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RADIATION_YN

Chemo Yes/No

uni_var(test_var = "CHEMO_YN", data_imp = data)
_________________________________________________
   
## CHEMO_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CHEMO_YN, data = data)

                  n events median 0.95LCL 0.95UCL
CHEMO_YN=No  342669  80729  164.6   164.4      NA
CHEMO_YN=Yes   9216   6400   16.6    15.8    17.2
CHEMO_YN=Ukn  11025   2843     NA   160.7      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CHEMO_YN, data = data)

                CHEMO_YN=No 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 289704   17488    0.946 0.000396        0.945        0.947
   24 249482   15703    0.893 0.000559        0.892        0.894
   36 206545   12671    0.844 0.000674        0.843        0.846
   48 170118    9390    0.803 0.000764        0.802        0.805
   60 137619    7033    0.767 0.000842        0.765        0.769
  120  32671   16359    0.623 0.001295        0.621        0.626

                CHEMO_YN=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5063    3783    0.581 0.00521        0.571        0.591
   24   3350    1399    0.415 0.00529        0.405        0.426
   36   2470     559    0.342 0.00518        0.332        0.353
   48   1923     272    0.303 0.00511        0.293        0.313
   60   1520     133    0.280 0.00509        0.270        0.290
  120    371     228    0.220 0.00551        0.210        0.231

                CHEMO_YN=Ukn 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   9390     676    0.936 0.00239        0.931        0.940
   24   8227     521    0.882 0.00321        0.876        0.888
   36   7064     421    0.835 0.00378        0.827        0.842
   48   6093     287    0.799 0.00417        0.791        0.807
   60   5163     244    0.765 0.00452        0.756        0.774
  120   1530     602    0.635 0.00635        0.622        0.647




   
## Univariable Cox Proportional Hazard Model for:  CHEMO_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CHEMO_YN, data = data)

  n= 362910, number of events= 89972 

                 coef exp(coef)  se(coef)       z Pr(>|z|)    
CHEMO_YNYes  1.670322  5.313878  0.013030 128.186   <2e-16 ***
CHEMO_YNUkn -0.003705  0.996302  0.019086  -0.194    0.846    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

            exp(coef) exp(-coef) lower .95 upper .95
CHEMO_YNYes    5.3139     0.1882    5.1799     5.451
CHEMO_YNUkn    0.9963     1.0037    0.9597     1.034

Concordance= 0.538  (se = 0 )
Rsquare= 0.029   (max possible= 0.998 )
Likelihood ratio test= 10647  on 2 df,   p=0
Wald test            = 16474  on 2 df,   p=0
Score (logrank) test = 20670  on 2 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  CHEMO_YN

Treatment Yes/No

uni_var(test_var = "Tx_YN", data_imp = data)
_________________________________________________
   
## Tx_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ Tx_YN, data = data)

   11025 observations deleted due to missingness 
                 n events median 0.95LCL 0.95UCL
Tx_YN=FALSE   8215   5411     12    11.2    12.8
Tx_YN=TRUE  343670  81718    165   164.4      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ Tx_YN, data = data)

11025 observations deleted due to missingness 
                Tx_YN=FALSE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3583    3896    0.500 0.00571        0.489        0.511
   24   2501     723    0.394 0.00570        0.383        0.405
   36   1796     340    0.336 0.00567        0.325        0.347
   48   1323     182    0.299 0.00568        0.288        0.310
   60   1017      90    0.276 0.00573        0.265        0.288
  120    212     166    0.208 0.00660        0.195        0.221

                Tx_YN=TRUE 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 291184   17375    0.946 0.000396        0.946        0.947
   24 250331   16379    0.891 0.000562        0.890        0.892
   36 207219   12890    0.842 0.000677        0.841        0.843
   48 170718    9480    0.801 0.000766        0.799        0.802
   60 138122    7076    0.765 0.000843        0.763        0.766
  120  32830   16421    0.621 0.001290        0.619        0.624




   
## Univariable Cox Proportional Hazard Model for:  Tx_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ Tx_YN, data = data)

  n= 351885, number of events= 87129 
   (11025 observations deleted due to missingness)

              coef exp(coef) se(coef)      z Pr(>|z|)    
Tx_YNTRUE -1.86336   0.15515  0.01408 -132.3   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

          exp(coef) exp(-coef) lower .95 upper .95
Tx_YNTRUE    0.1552      6.445    0.1509    0.1595

Concordance= 0.537  (se = 0 )
Rsquare= 0.03   (max possible= 0.998 )
Likelihood ratio test= 10747  on 1 df,   p=0
Wald test            = 17504  on 1 df,   p=0
Score (logrank) test = 23183  on 1 df,   p=0





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  Tx_YN

Metastases at Dx

uni_var(test_var = "mets_at_dx_F", data_imp = data)
_________________________________________________
   
## mets_at_dx_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ mets_at_dx_F, data = data)

                        n events median 0.95LCL 0.95UCL
mets_at_dx_F=FALSE 357195  85269 164.57  162.89      NA
mets_at_dx_F=TRUE    5715   4703   5.91    5.62    6.14

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ mets_at_dx_F, data = data)

                mets_at_dx_F=FALSE 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 302553   18162    0.946 0.000389        0.945        0.947
   24 260282   17003    0.891 0.000551        0.890        0.892
   36 215704   13444    0.842 0.000664        0.840        0.843
   48 177949    9891    0.800 0.000751        0.799        0.802
   60 144215    7387    0.764 0.000826        0.763        0.766
  120  34572   17179    0.621 0.001260        0.619        0.624

                mets_at_dx_F=TRUE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1604    3785   0.3137 0.00631       0.3015       0.3263
   24    777     620   0.1841 0.00547       0.1737       0.1951
   36    375     207   0.1265 0.00505       0.1170       0.1368
   48    185      58   0.1030 0.00499       0.0936       0.1132
   60     87      23   0.0863 0.00530       0.0765       0.0974




   
## Univariable Cox Proportional Hazard Model for:  mets_at_dx_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ mets_at_dx_F, data = data)

  n= 362910, number of events= 89972 

                     coef exp(coef) se(coef)   z Pr(>|z|)    
mets_at_dx_FTRUE  2.76699  15.91061  0.01537 180   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                 exp(coef) exp(-coef) lower .95 upper .95
mets_at_dx_FTRUE     15.91    0.06285     15.44      16.4

Concordance= 0.535  (se = 0 )
Rsquare= 0.045   (max possible= 0.998 )
Likelihood ratio test= 16759  on 1 df,   p=0
Wald test            = 32390  on 1 df,   p=0
Score (logrank) test = 58926  on 1 df,   p=0





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  mets_at_dx_F

Tumor specific Variables

Node Size

Cox Proportional Hazard Ratio

Model #1

Full analysis

model_one <- coxph(Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)
                     ~ SURG_RAD_SEQ + INSURANCE_F + AGE + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F,
                     data = data)
X matrix deemed to be singular; variable 11
model_one %>% summary()
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ + INSURANCE_F + AGE + SEX_F + RACE_F + 
    INCOME_F + U_R_F + FACILITY_TYPE_F + FACILITY_LOCATION_F + 
    EDUCATION_F, data = data)

  n= 310618, number of events= 84098 
   (52292 observations deleted due to missingness)

                                                  coef exp(coef)  se(coef)       z Pr(>|z|)    
SURG_RAD_SEQSurg then Rad                     1.175093  3.238443  0.017436  67.395  < 2e-16 ***
SURG_RAD_SEQRad Alone                         2.488459 12.042700  0.016545 150.404  < 2e-16 ***
SURG_RAD_SEQNo Treatment                      1.918921  6.813599  0.012812 149.775  < 2e-16 ***
SURG_RAD_SEQOther                             0.527635  1.694918  0.031302  16.856  < 2e-16 ***
SURG_RAD_SEQRad before and after Surg         2.664354 14.358675  0.288773   9.226  < 2e-16 ***
SURG_RAD_SEQRad then Surg                     1.634362  5.126184  0.126140  12.957  < 2e-16 ***
INSURANCE_FNone                               0.816196  2.261881  0.023242  35.117  < 2e-16 ***
INSURANCE_FMedicaid                           0.950788  2.587747  0.021840  43.535  < 2e-16 ***
INSURANCE_FMedicare                           0.172193  1.187907  0.009972  17.268  < 2e-16 ***
INSURANCE_FOther Government                   0.226541  1.254254  0.037211   6.088 1.14e-09 ***
INSURANCE_FUnknown                                  NA        NA  0.000000      NA       NA    
AGE                                           0.055292  1.056849  0.000389 142.146  < 2e-16 ***
SEX_FFemale                                  -0.317842  0.727718  0.007424 -42.811  < 2e-16 ***
RACE_FBlack                                   0.402027  1.494852  0.035050  11.470  < 2e-16 ***
RACE_FOther/Unk                              -0.089698  0.914207  0.030340  -2.956 0.003112 ** 
RACE_FAsian                                   0.228528  1.256749  0.061677   3.705 0.000211 ***
INCOME_F$38,000 - $47,999                    -0.047120  0.953973  0.012448  -3.785 0.000153 ***
INCOME_F$48,000 - $62,999                    -0.080485  0.922669  0.013199  -6.098 1.08e-09 ***
INCOME_F$63,000 +                            -0.148064  0.862376  0.014837  -9.979  < 2e-16 ***
U_R_FUrban                                   -0.035235  0.965379  0.010583  -3.329 0.000871 ***
U_R_FRural                                   -0.011491  0.988575  0.024840  -0.463 0.643647    
FACILITY_TYPE_FComprehensive Comm Ca Program -0.046446  0.954616  0.013827  -3.359 0.000782 ***
FACILITY_TYPE_FAcademic/Research Program     -0.137317  0.871694  0.013803  -9.949  < 2e-16 ***
FACILITY_TYPE_FIntegrated Network Ca Program -0.019513  0.980677  0.016205  -1.204 0.228544    
FACILITY_LOCATION_FMiddle Atlantic           -0.004003  0.996005  0.016806  -0.238 0.811730    
FACILITY_LOCATION_FSouth Atlantic            -0.027797  0.972586  0.016273  -1.708 0.087608 .  
FACILITY_LOCATION_FEast North Central         0.073783  1.076573  0.016696   4.419 9.90e-06 ***
FACILITY_LOCATION_FEast South Central         0.062075  1.064043  0.019632   3.162 0.001567 ** 
FACILITY_LOCATION_FWest North Central         0.033008  1.033559  0.019111   1.727 0.084140 .  
FACILITY_LOCATION_FWest South Central         0.015189  1.015304  0.020821   0.729 0.465706    
FACILITY_LOCATION_FMountain                   0.021827  1.022067  0.021098   1.035 0.300893    
FACILITY_LOCATION_FPacific                   -0.053757  0.947662  0.017688  -3.039 0.002372 ** 
EDUCATION_F13 - 20.9%                        -0.067335  0.934882  0.012642  -5.326 1.00e-07 ***
EDUCATION_F7 - 12.9%                         -0.117863  0.888818  0.013357  -8.824  < 2e-16 ***
EDUCATION_FLess than 7%                      -0.255588  0.774461  0.015259 -16.750  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                             exp(coef) exp(-coef) lower .95 upper .95
SURG_RAD_SEQSurg then Rad                       3.2384    0.30879    3.1296    3.3510
SURG_RAD_SEQRad Alone                          12.0427    0.08304   11.6584   12.4396
SURG_RAD_SEQNo Treatment                        6.8136    0.14677    6.6446    6.9869
SURG_RAD_SEQOther                               1.6949    0.59000    1.5941    1.8022
SURG_RAD_SEQRad before and after Surg          14.3587    0.06964    8.1529   25.2882
SURG_RAD_SEQRad then Surg                       5.1262    0.19508    4.0033    6.5639
INSURANCE_FNone                                 2.2619    0.44211    2.1612    2.3673
INSURANCE_FMedicaid                             2.5877    0.38644    2.4793    2.7009
INSURANCE_FMedicare                             1.1879    0.84182    1.1649    1.2114
INSURANCE_FOther Government                     1.2543    0.79729    1.1660    1.3491
INSURANCE_FUnknown                                  NA         NA        NA        NA
AGE                                             1.0568    0.94621    1.0560    1.0577
SEX_FFemale                                     0.7277    1.37416    0.7172    0.7384
RACE_FBlack                                     1.4949    0.66896    1.3956    1.6012
RACE_FOther/Unk                                 0.9142    1.09384    0.8614    0.9702
RACE_FAsian                                     1.2567    0.79570    1.1136    1.4182
INCOME_F$38,000 - $47,999                       0.9540    1.04825    0.9310    0.9775
INCOME_F$48,000 - $62,999                       0.9227    1.08381    0.8991    0.9468
INCOME_F$63,000 +                               0.8624    1.15959    0.8377    0.8878
U_R_FUrban                                      0.9654    1.03586    0.9456    0.9856
U_R_FRural                                      0.9886    1.01156    0.9416    1.0379
FACILITY_TYPE_FComprehensive Comm Ca Program    0.9546    1.04754    0.9291    0.9808
FACILITY_TYPE_FAcademic/Research Program        0.8717    1.14719    0.8484    0.8956
FACILITY_TYPE_FIntegrated Network Ca Program    0.9807    1.01970    0.9500    1.0123
FACILITY_LOCATION_FMiddle Atlantic              0.9960    1.00401    0.9637    1.0294
FACILITY_LOCATION_FSouth Atlantic               0.9726    1.02819    0.9421    1.0041
FACILITY_LOCATION_FEast North Central           1.0766    0.92887    1.0419    1.1124
FACILITY_LOCATION_FEast South Central           1.0640    0.93981    1.0239    1.1058
FACILITY_LOCATION_FWest North Central           1.0336    0.96753    0.9956    1.0730
FACILITY_LOCATION_FWest South Central           1.0153    0.98493    0.9747    1.0576
FACILITY_LOCATION_FMountain                     1.0221    0.97841    0.9807    1.0652
FACILITY_LOCATION_FPacific                      0.9477    1.05523    0.9154    0.9811
EDUCATION_F13 - 20.9%                           0.9349    1.06965    0.9120    0.9583
EDUCATION_F7 - 12.9%                            0.8888    1.12509    0.8659    0.9124
EDUCATION_FLess than 7%                         0.7745    1.29122    0.7516    0.7980

Concordance= 0.767  (se = 0.001 )
Rsquare= 0.226   (max possible= 0.999 )
Likelihood ratio test= 79560  on 34 df,   p=0
Wald test            = 91341  on 34 df,   p=0
Score (logrank) test = 120014  on 34 df,   p=0

Summary of Model

model_one %>%
        tidy(., exponentiate = TRUE) %>%
        select(term, estimate, conf.low, conf.high, p.value) %>%
        rename(Variable = term,
               Hazard_Ratio = estimate) %>%
        tbl_df %>%
        print(n = nrow(.))

Predictors of Surgery

fit_surg <- glm(SURG_TF ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP,
   data = data %>% filter(SURGERY_YN != "Ukn") %>% droplevels() %>% mutate(SURGERY_YN = as.logical(SURGERY_YN)))
summary(fit_surg)

Call:
glm(formula = SURG_TF ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F + 
    FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP, 
    data = data %>% filter(SURGERY_YN != "Ukn") %>% droplevels() %>% 
        mutate(SURGERY_YN = as.logical(SURGERY_YN)))

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-0.99423   0.03190   0.04440   0.05726   0.21648  

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                   0.9081399  0.0028620 317.304  < 2e-16 ***
AGE_F(54,64]                                 -0.0072041  0.0010786  -6.679 2.41e-11 ***
AGE_F(64,74]                                 -0.0081639  0.0010934  -7.466 8.27e-14 ***
AGE_F(74,100]                                -0.0200655  0.0010744 -18.675  < 2e-16 ***
SEX_FFemale                                   0.0109075  0.0007849  13.896  < 2e-16 ***
RACE_FBlack                                  -0.0929640  0.0049996 -18.594  < 2e-16 ***
RACE_FOther/Unk                              -0.0006775  0.0030515  -0.222 0.824293    
RACE_FAsian                                  -0.0518461  0.0074247  -6.983 2.90e-12 ***
INCOME_F$38,000 - $47,999                     0.0041661  0.0015199   2.741 0.006124 ** 
INCOME_F$48,000 - $62,999                     0.0085346  0.0015873   5.377 7.59e-08 ***
INCOME_F$63,000 +                             0.0110084  0.0017396   6.328 2.49e-10 ***
U_R_FUrban                                    0.0050951  0.0012133   4.199 2.68e-05 ***
U_R_FRural                                    0.0025631  0.0029320   0.874 0.382028    
FACILITY_TYPE_FComprehensive Comm Ca Program  0.0246966  0.0016691  14.796  < 2e-16 ***
FACILITY_TYPE_FAcademic/Research Program      0.0392540  0.0016438  23.880  < 2e-16 ***
FACILITY_TYPE_FIntegrated Network Ca Program  0.0254413  0.0019155  13.282  < 2e-16 ***
FACILITY_LOCATION_FMiddle Atlantic           -0.0034108  0.0018257  -1.868 0.061737 .  
FACILITY_LOCATION_FSouth Atlantic            -0.0059582  0.0018019  -3.307 0.000944 ***
FACILITY_LOCATION_FEast North Central        -0.0053532  0.0018364  -2.915 0.003556 ** 
FACILITY_LOCATION_FEast South Central        -0.0035544  0.0022293  -1.594 0.110845    
FACILITY_LOCATION_FWest North Central        -0.0093962  0.0020616  -4.558 5.18e-06 ***
FACILITY_LOCATION_FWest South Central        -0.0253217  0.0023654 -10.705  < 2e-16 ***
FACILITY_LOCATION_FMountain                  -0.0120912  0.0023058  -5.244 1.57e-07 ***
FACILITY_LOCATION_FPacific                   -0.0098457  0.0019394  -5.077 3.84e-07 ***
EDUCATION_F13 - 20.9%                         0.0067553  0.0015322   4.409 1.04e-05 ***
EDUCATION_F7 - 12.9%                          0.0114129  0.0015965   7.149 8.78e-13 ***
EDUCATION_FLess than 7%                       0.0162856  0.0017663   9.220  < 2e-16 ***
EXPN_GROUPPre-Expansion                       0.0086333  0.0011013   7.840 4.54e-15 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.04499771)

    Null deviance: 14065  on 309989  degrees of freedom
Residual deviance: 13948  on 309962  degrees of freedom
  (52236 observations deleted due to missingness)
AIC: -81580

Number of Fisher Scoring iterations: 2
exp(cbind("Odds ratio" = coef(fit_surg), confint.default(fit_surg, level = 0.95)))
                                             Odds ratio     2.5 %    97.5 %
(Intercept)                                   2.4797057 2.4658347 2.4936548
AGE_F(54,64]                                  0.9928218 0.9907252 0.9949229
AGE_F(64,74]                                  0.9918693 0.9897459 0.9939973
AGE_F(74,100]                                 0.9801345 0.9780726 0.9822007
SEX_FFemale                                   1.0109672 1.0094131 1.0125237
RACE_FBlack                                   0.9112263 0.9023407 0.9201994
RACE_FOther/Unk                               0.9993227 0.9933638 1.0053174
RACE_FAsian                                   0.9494750 0.9357581 0.9633928
INCOME_F$38,000 - $47,999                     1.0041748 1.0011879 1.0071707
INCOME_F$48,000 - $62,999                     1.0085711 1.0054382 1.0117137
INCOME_F$63,000 +                             1.0110692 1.0076277 1.0145225
U_R_FUrban                                    1.0051081 1.0027208 1.0075010
U_R_FRural                                    1.0025663 0.9968215 1.0083443
FACILITY_TYPE_FComprehensive Comm Ca Program  1.0250041 1.0216563 1.0283628
FACILITY_TYPE_FAcademic/Research Program      1.0400346 1.0366892 1.0433908
FACILITY_TYPE_FIntegrated Network Ca Program  1.0257677 1.0219238 1.0296261
FACILITY_LOCATION_FMiddle Atlantic            0.9965950 0.9930353 1.0001676
FACILITY_LOCATION_FSouth Atlantic             0.9940596 0.9905551 0.9975764
FACILITY_LOCATION_FEast North Central         0.9946611 0.9910876 0.9982476
FACILITY_LOCATION_FEast South Central         0.9964519 0.9921076 1.0008152
FACILITY_LOCATION_FWest North Central         0.9906478 0.9866529 0.9946588
FACILITY_LOCATION_FWest South Central         0.9749962 0.9704864 0.9795270
FACILITY_LOCATION_FMountain                   0.9879816 0.9835266 0.9924568
FACILITY_LOCATION_FPacific                    0.9902026 0.9864458 0.9939737
EDUCATION_F13 - 20.9%                         1.0067782 1.0037594 1.0098061
EDUCATION_F7 - 12.9%                          1.0114782 1.0083182 1.0146482
EDUCATION_FLess than 7%                       1.0164189 1.0129062 1.0199438
EXPN_GROUPPre-Expansion                       1.0086707 1.0064959 1.0108502

Predictors of Metastasis at Time of Diagnosis, limit to those cases where data

about expansion status is available (> Age 39, non-ambiguous status states)

fit_mets <- glm(mets_at_dx_F ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP,
   data = no_Excludes)
summary(fit_mets)

Call:
glm(formula = mets_at_dx_F ~ AGE_F + SEX_F + RACE_F + INCOME_F + 
    U_R_F + FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + 
    EXPN_GROUP, data = no_Excludes)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-0.08216  -0.02878  -0.02092  -0.01372   1.00266  

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                   0.0495593  0.0019342  25.623  < 2e-16 ***
AGE_F(54,64]                                  0.0049569  0.0007282   6.807 1.00e-11 ***
AGE_F(64,74]                                  0.0057453  0.0007388   7.777 7.48e-15 ***
AGE_F(74,100]                                 0.0084310  0.0007270  11.598  < 2e-16 ***
SEX_FFemale                                  -0.0085557  0.0005312 -16.106  < 2e-16 ***
RACE_FBlack                                   0.0223772  0.0033314   6.717 1.86e-11 ***
RACE_FOther/Unk                              -0.0016802  0.0020669  -0.813 0.416276    
RACE_FAsian                                   0.0237505  0.0050021   4.748 2.05e-06 ***
INCOME_F$38,000 - $47,999                    -0.0010712  0.0010236  -1.046 0.295348    
INCOME_F$48,000 - $62,999                    -0.0028864  0.0010698  -2.698 0.006975 ** 
INCOME_F$63,000 +                            -0.0063578  0.0011735  -5.418 6.04e-08 ***
U_R_FUrban                                   -0.0022755  0.0008187  -2.780 0.005444 ** 
U_R_FRural                                    0.0018322  0.0019826   0.924 0.355399    
FACILITY_TYPE_FComprehensive Comm Ca Program -0.0064353  0.0011296  -5.697 1.22e-08 ***
FACILITY_TYPE_FAcademic/Research Program     -0.0098655  0.0011124  -8.869  < 2e-16 ***
FACILITY_TYPE_FIntegrated Network Ca Program -0.0025561  0.0012934  -1.976 0.048125 *  
FACILITY_LOCATION_FMiddle Atlantic            0.0017956  0.0012371   1.451 0.146646    
FACILITY_LOCATION_FSouth Atlantic             0.0049151  0.0012196   4.030 5.58e-05 ***
FACILITY_LOCATION_FEast North Central         0.0045108  0.0012441   3.626 0.000288 ***
FACILITY_LOCATION_FEast South Central         0.0053204  0.0015035   3.539 0.000402 ***
FACILITY_LOCATION_FWest North Central         0.0015550  0.0013967   1.113 0.265582    
FACILITY_LOCATION_FWest South Central         0.0146708  0.0015962   9.191  < 2e-16 ***
FACILITY_LOCATION_FMountain                   0.0084461  0.0015581   5.421 5.94e-08 ***
FACILITY_LOCATION_FPacific                   -0.0015244  0.0013144  -1.160 0.246137    
EDUCATION_F13 - 20.9%                        -0.0004141  0.0010313  -0.402 0.688029    
EDUCATION_F7 - 12.9%                         -0.0032919  0.0010757  -3.060 0.002212 ** 
EDUCATION_FLess than 7%                      -0.0051212  0.0011914  -4.299 1.72e-05 ***
EXPN_GROUPPre-Expansion                      -0.0224662  0.0007473 -30.063  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.02156817)

    Null deviance: 7052.0  on 325127  degrees of freedom
Residual deviance: 7011.8  on 325100  degrees of freedom
  (10945 observations deleted due to missingness)
AIC: -324662

Number of Fisher Scoring iterations: 2
exp(cbind("Odds ratio" = coef(fit_mets), confint.default(fit_surg, level = 0.95)))
                                             Odds ratio     2.5 %    97.5 %
(Intercept)                                   1.0508079 2.4658347 2.4936548
AGE_F(54,64]                                  1.0049692 0.9907252 0.9949229
AGE_F(64,74]                                  1.0057619 0.9897459 0.9939973
AGE_F(74,100]                                 1.0084667 0.9780726 0.9822007
SEX_FFemale                                   0.9914808 1.0094131 1.0125237
RACE_FBlack                                   1.0226295 0.9023407 0.9201994
RACE_FOther/Unk                               0.9983212 0.9933638 1.0053174
RACE_FAsian                                   1.0240347 0.9357581 0.9633928
INCOME_F$38,000 - $47,999                     0.9989294 1.0011879 1.0071707
INCOME_F$48,000 - $62,999                     0.9971178 1.0054382 1.0117137
INCOME_F$63,000 +                             0.9936624 1.0076277 1.0145225
U_R_FUrban                                    0.9977271 1.0027208 1.0075010
U_R_FRural                                    1.0018339 0.9968215 1.0083443
FACILITY_TYPE_FComprehensive Comm Ca Program  0.9935853 1.0216563 1.0283628
FACILITY_TYPE_FAcademic/Research Program      0.9901830 1.0366892 1.0433908
FACILITY_TYPE_FIntegrated Network Ca Program  0.9974472 1.0219238 1.0296261
FACILITY_LOCATION_FMiddle Atlantic            1.0017972 0.9930353 1.0001676
FACILITY_LOCATION_FSouth Atlantic             1.0049272 0.9905551 0.9975764
FACILITY_LOCATION_FEast North Central         1.0045209 0.9910876 0.9982476
FACILITY_LOCATION_FEast South Central         1.0053346 0.9921076 1.0008152
FACILITY_LOCATION_FWest North Central         1.0015562 0.9866529 0.9946588
FACILITY_LOCATION_FWest South Central         1.0147790 0.9704864 0.9795270
FACILITY_LOCATION_FMountain                   1.0084818 0.9835266 0.9924568
FACILITY_LOCATION_FPacific                    0.9984767 0.9864458 0.9939737
EDUCATION_F13 - 20.9%                         0.9995860 1.0037594 1.0098061
EDUCATION_F7 - 12.9%                          0.9967135 1.0083182 1.0146482
EDUCATION_FLess than 7%                       0.9948919 1.0129062 1.0199438
EXPN_GROUPPre-Expansion                       0.9777843 1.0064959 1.0108502
---
title: "Derm NCDB"
author: "Ramie Fathy"
date: "10/7/2019"
output:
  html_notebook: default
  pdf_document: default
---



```{r chunk1, echo=FALSE, warning=FALSE, message=FALSE}

library("ggplot2")
library("dplyr")
library("tidyr")
library("knitr")
library("tableone")
library("forcats")
library("survival")
library("npsurv")
library("broom")
library("tibble")
library("readr")
library("survminer")
library("stringr")

knitr::opts_chunk$set(echo=TRUE, warning=FALSE, message=TRUE)
'%!in%' <- function(x,y)!('%in%'(x,y))
```

```{r chunk2, cache=TRUE, message=FALSE, warning=FALSE, results='hide'}
col.width <- c(37, 10, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 4, 4, 1, 4, 1, 1,
               1, 3, 2, 2, 8, 2, 5, 5, 5, 4, 5, 5, 5,4, 2, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3,
               3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 8,
               8, 8, 2, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 2, 2, 5, 2, 5, 3, 1, 3, 1, 8, 8, 2, 8,
               2, 8, 2, 2, 1, 8, 1, 1, 1, 1, 1, 8, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 3, 1, 1,
               1, 1, 1, 1, 1, 1, 1)

col.names.abr <- c("PUF_CASE_ID", "PUF_FACILITY_ID", "FACILITY_TYPE_CD", "FACILITY_LOCATION_CD",
                   "AGE", "SEX", "RACE", "SPANISH_HISPANIC_ORIGIN", "INSURANCE_STATUS",
                   "MED_INC_QUAR_00", "NO_HSD_QUAR_00", "UR_CD_03", "MED_INC_QUAR_12", "NO_HSD_QUAR_12",
                   "UR_CD_13", "CROWFLY", "CDCC_TOTAL_BEST", "SEQUENCE_NUMBER", "CLASS_OF_CASE",
                   "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "LATERALITY", "HISTOLOGY", "BEHAVIOR", "GRADE",
                   "DIAGNOSTIC_CONFIRMATION", "TUMOR_SIZE", "REGIONAL_NODES_POSITIVE",
                   "REGIONAL_NODES_EXAMINED", "DX_STAGING_PROC_DAYS", "RX_SUMM_DXSTG_PROC", "TNM_CLIN_T",
                   "TNM_CLIN_N", "TNM_CLIN_M", "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                   "TNM_PATH_STAGE_GROUP", "TNM_EDITION_NUMBER", "ANALYTIC_STAGE_GROUP", "CS_METS_AT_DX",
                   "CS_METS_EVAL", "CS_EXTENSION", "CS_TUMOR_SIZEEXT_EVAL", "CS_METS_DX_BONE", "CS_METS_DX_BRAIN",
                   "CS_METS_DX_LIVER", "CS_METS_DX_LUNG", "LYMPH_VASCULAR_INVASION", "CS_SITESPECIFIC_FACTOR_1",
                   "CS_SITESPECIFIC_FACTOR_2", "CS_SITESPECIFIC_FACTOR_3", "CS_SITESPECIFIC_FACTOR_4",
                   "CS_SITESPECIFIC_FACTOR_5", "CS_SITESPECIFIC_FACTOR_6", "CS_SITESPECIFIC_FACTOR_7",
                   "CS_SITESPECIFIC_FACTOR_8", "CS_SITESPECIFIC_FACTOR_9", "CS_SITESPECIFIC_FACTOR_10",
                   "CS_SITESPECIFIC_FACTOR_11", "CS_SITESPECIFIC_FACTOR_12", "CS_SITESPECIFIC_FACTOR_13",
                   "CS_SITESPECIFIC_FACTOR_14", "CS_SITESPECIFIC_FACTOR_15", "CS_SITESPECIFIC_FACTOR_16",
                   "CS_SITESPECIFIC_FACTOR_17", "CS_SITESPECIFIC_FACTOR_18", "CS_SITESPECIFIC_FACTOR_19",
                   "CS_SITESPECIFIC_FACTOR_20", "CS_SITESPECIFIC_FACTOR_21", "CS_SITESPECIFIC_FACTOR_22",
                   "CS_SITESPECIFIC_FACTOR_23", "CS_SITESPECIFIC_FACTOR_24", "CS_SITESPECIFIC_FACTOR_25",
                   "CS_VERSION_LATEST", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS", "DX_DEFSURG_STARTED_DAYS",
                   "RX_SUMM_SURG_PRIM_SITE", "RX_HOSP_SURG_APPR_2010", "RX_SUMM_SURGICAL_MARGINS",
                   "RX_SUMM_SCOPE_REG_LN_SUR", "RX_SUMM_SURG_OTH_REGDIS", "SURG_DISCHARGE_DAYS", "READM_HOSP_30_DAYS",
                   "REASON_FOR_NO_SURGERY", "DX_RAD_STARTED_DAYS", "RX_SUMM_RADIATION", "RAD_LOCATION_OF_RX",
                   "RAD_TREAT_VOL", "RAD_REGIONAL_RX_MODALITY", "RAD_REGIONAL_DOSE_CGY", "RAD_BOOST_RX_MODALITY",
                   "RAD_BOOST_DOSE_CGY", "RAD_NUM_TREAT_VOL", "RX_SUMM_SURGRAD_SEQ", "RAD_ELAPSED_RX_DAYS",
                   "REASON_FOR_NO_RADIATION", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", "RX_SUMM_CHEMO",
                   "DX_HORMONE_STARTED_DAYS", "RX_SUMM_HORMONE", "DX_IMMUNO_STARTED_DAYS", "RX_SUMM_IMMUNOTHERAPY",
                   "RX_SUMM_TRNSPLNT_ENDO", "RX_SUMM_SYSTEMIC_SUR_SEQ", "DX_OTHER_STARTED_DAYS", "RX_SUMM_OTHER",
                   "PALLIATIVE_CARE", "RX_SUMM_TREATMENT_STATUS", "PUF_30_DAY_MORT_CD", "PUF_90_DAY_MORT_CD",
                   "DX_LASTCONTACT_DEATH_MONTHS", "PUF_VITAL_STATUS", "RX_HOSP_SURG_PRIM_SITE", "RX_HOSP_CHEMO",
                   "RX_HOSP_IMMUNOTHERAPY", "RX_HOSP_HORMONE", "RX_HOSP_OTHER", "PUF_MULT_SOURCE", "REFERENCE_DATE_FLAG",
                   "RX_SUMM_SCOPE_REG_LN_2012", "RX_HOSP_DXSTG_PROC", "PALLIATIVE_CARE_HOSP", "TUMOR_SIZE_SUMMARY",
                   "METS_AT_DX_OTHER", "METS_AT_DX_DISTANT_LN", "METS_AT_DX_BONE", "METS_AT_DX_BRAIN",
                   "METS_AT_DX_LIVER", "METS_AT_DX_LUNG", "NO_HSD_QUAR_16", "MED_INC_QUAR_16", "MEDICAID_EXPN_CODE")



#Read in data for each subsite
lip <- read_fwf('NCDBPUF_Lip.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

melanoma <- read_fwf('NCDBPUF_Melanoma.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
                       
skin <- read_fwf('NCDBPUF_OtSkin.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

hodgextr <- read_fwf('NCDBPUF_HodgExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

hodgndal <- read_fwf('NCDBPUF_HodgNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

NHLndal <- read_fwf('NCDBPUF_NHLNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))



#Combine data for all subsites
dat <- bind_rows(lip, melanoma, skin, hodgextr, hodgndal, NHLndal)

rm(lip, melanoma, skin, hodgextr, hodgndal, NHLndal)


#only works for melanoma
prim_site_text <- data_frame(PRIMARY_SITE = c("C440",
"C441",
"C442",
"C443",
"C444",
"C445",
"C446",
"C447",
"C448",
"C449", 
"C510",
"C511",
"C512",
"C518",
"C519",
"C600",
"C601",
"C602",
"C608",
"C609",
"C632"),                
SITE_TEXT = c(
"C44.0 Skin of lip, NOS",
"C44.1 Eyelid",
"C44.2 External ear",
"C44.3 Skin of ear and unspecified parts of face",
"C44.4 Skin of scalp and neck",
"C44.5 Skin of trunk",
"C44.6 Skin of upper limb and shoulder",
"C44.7 Skin of lower limb and hip",
"C44.8 Overlapping lesion of skin",
"C44.9 Skin, NOS", 
"C51.0 Labium majus",
"C51.1 Labium minus",
"C51.2 Clitoris",
"C51.8 Overlapping lesion of vulva",
"C51.9 Vulva, NOS",
"C60.0 Prepuce",
"C60.1 Glans penis",
"C60.2 Body of penis",
"C60.8 Overlapping lesion of penis",
"C60.9 Penis",
"C63.2 Scrotum, NOS"))


dat <- merge(dat, prim_site_text, by = "PRIMARY_SITE", all.x = TRUE) 
 
rm(prim_site_text)

# convert numeric variables from character class to numeric class
num_vars <- c("AGE", "CROWFLY", "TUMOR_SIZE", "DX_STAGING_PROC_DAYS", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
              "DX_DEFSURG_STARTED_DAYS", "SURG_DISCHARGE_DAYS", "DX_RAD_STARTED_DAYS",  "RAD_REGIONAL_DOSE_CGY",
              "RAD_BOOST_DOSE_CGY", "RAD_ELAPSED_RX_DAYS", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", 
              "DX_HORMONE_STARTED_DAYS", "DX_OTHER_STARTED_DAYS", "DX_LASTCONTACT_DEATH_MONTHS",
              "RAD_NUM_TREAT_VOL")

dat[num_vars] <- lapply(dat[num_vars], as.numeric)


# convert factor variables from character class to factor class
vars <- names(dat)
fact_vars <- vars[!(vars %in% num_vars)] # basically all of the non-numerics

dat[fact_vars] <- lapply(dat[fact_vars], as.character)
dat[fact_vars] <- lapply(dat[fact_vars], as.factor)

dat <- dat %>%
        mutate(FACILITY_TYPE_F = fct_recode(FACILITY_TYPE_CD,
                                            "Community Cancer Program" = "1",
                                            "Comprehensive Comm Ca Program" = "2",
                                            "Academic/Research Program" = "3",
                                            "Integrated Network Ca Program" = "4",
                                            "Other" = "9")) %>%
        mutate(FACILITY_LOCATION_F = fct_recode(FACILITY_LOCATION_CD,
                                            "New England" = "1",
                                            "Middle Atlantic" = "2",
                                            "South Atlantic" = "3",
                                            "East North Central" = "4",
                                            "East South Central" = "5",
                                            "West North Central" = "6",
                                            "West South Central" = "7",
                                            "Mountain" = "8",
                                            "Pacific" = "9",
                                            "out of US" = "0")) %>%
        mutate(FACILITY_GEOGRAPHY = fct_collapse(FACILITY_LOCATION_CD,
                                                 "Northeast" = c("1", "2"),
                                                 "South" = c("3", "7"),
                                                 "Midwest" = c("4", "5", "6"),
                                                 "West" = c("8", "9"))) %>%
        mutate(AGE_F = cut(AGE, c(0, 54, 64, 74, 100))) %>%
        mutate(AGE_40 = cut(AGE, c(0, 40, 100))) %>%
        mutate(SEX_F = fct_recode(SEX,
                                "Male" = "1",
                                "Female" = "2")) %>%
        mutate(RACE_F = fct_collapse(RACE,
                                "White" = c("01"),
                                "Black" = c("02"),
                                "Asian" = c("04", "05", "06", "07", "08", "10", "11", "12", "13", "14", "15",
                                            "16", "17", "20", "21", "22", "25", "26", "27", "28", "30", "31",
                                            "32", "96", "97"),
                                "Other/Unk" = c("03", "98", "99"))) %>%
        mutate(HISPANIC = fct_collapse(SPANISH_HISPANIC_ORIGIN,
                                       "Yes" = c("1", "2", "3", "4", "5", "6", "7", "8"),
                                       "No" = c("0"),
                                       "Unknown" = c("9"))) %>%
        mutate(INSURANCE_F = fct_recode(INSURANCE_STATUS,
                                         "None" = "0",
                                         "Private" = "1",
                                         "Medicaid" = "2",
                                         "Medicare" = "3",
                                         "Other Government" = "4",
                                         "Unknown" = "9")) %>%
        mutate(INSURANCE_F = fct_relevel(INSURANCE_F,
                                         "Private")) %>%
        mutate(INCOME_F = fct_recode(MED_INC_QUAR_12,
                                      "Less than $38,000" = "1",
                                      "$38,000 - $47,999" = "2",
                                      "$48,000 - $62,999" = "3",
                                      "$63,000 +" = "4")) %>%
        mutate(EDUCATION_F = fct_recode(NO_HSD_QUAR_12,
                                        "21% or more" = "1",
                                        "13 - 20.9%" = "2",
                                        "7 - 12.9%" = "3",
                                        "Less than 7%" = "4")) %>%
        mutate(U_R_F = fct_collapse(UR_CD_13,
                                    "Metro" = c("1", "2", "3"),
                                    "Urban" = c("4", "5", "6", "7"),
                                    "Rural" = c("8", "9"))) %>%
        mutate(CLASS_OF_CASE_F = fct_collapse(CLASS_OF_CASE,
                                              All_Part_Prim = c("10", "11", "12", "13",
                                                                "14", "20", "21", "22"),
                                              Other_Facility = c("00"))) %>%
        mutate(GRADE_F = fct_recode(GRADE,
                                  "Gr I: Well Diff" = "1",
                                  "Gr II: Mod Diff" = "2",
                                  "Gr III: Poor Diff" = "3",
                                  "Gr IV: Undiff/Anaplastic" = "4",
                                  "NA/Unkown" = "9")) %>%
        mutate(HISTOLOGY_F = fct_infreq(HISTOLOGY)) %>%
        mutate(HISTOLOGY_F = factor(HISTOLOGY_F)) %>%
        mutate(HISTOLOGY_F_LIM = fct_lump(HISTOLOGY_F, prop = 0.05)) %>%
        mutate(TNM_CLIN_T = fct_recode(TNM_CLIN_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_T = fct_relevel(TNM_CLIN_T,
                                        "1")) %>%
        mutate(TNM_CLIN_N = fct_recode(TNM_CLIN_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_M = fct_recode(TNM_CLIN_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_recode(TNM_PATH_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_relevel(TNM_PATH_T,
                                        "1")) %>%
        mutate(TNM_PATH_N = fct_recode(TNM_PATH_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_M = fct_recode(TNM_PATH_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_STAGE_GROUP = fct_recode(TNM_CLIN_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_STAGE_GROUP = fct_recode(TNM_PATH_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(MARGINS = fct_recode(RX_SUMM_SURGICAL_MARGINS,
                                    "No Residual" = "0",
                                    "Residual, NOS" = "1",
                                    "Microscopic Resid" = "2",
                                    "Macroscopic Resid" = "3",
                                    "Not evaluable" = "7",
                                    "No surg" = "8",
                                    "Unknown" = "9")) %>%
        mutate(MARGINS_YN = fct_collapse(RX_SUMM_SURGICAL_MARGINS,
                                         "Yes" = c("1", "2", "3"),
                                         "No" = c("0"),
                                         "No surg/Unk/NA" = c("7", "8", "9"))) %>%
        mutate(READM_HOSP_30_DAYS_F = fct_recode(READM_HOSP_30_DAYS,
                                                 "No_Surg_or_No_Readmit" = "0",
                                                 "Unplan_Readmit_Same" = "1",
                                                 "Plan_Readmit_Same" = "2",
                                                 "PlanUnplan_Same" = "3",
                                                 "Unknown" = "4")) %>%
        mutate(RX_SUMM_RADIATION_F = fct_recode(RX_SUMM_RADIATION,
                                                "None" = "0",
                                                "Beam Radiation" = "1",
                                                "Radioactive Implants" = "2",
                                                "Radioisotopes" = "3",
                                                "Beam + Imp or Isotopes" = "4",
                                                "Radiation, NOS" = "5",
                                                "Unknown" = "9")) %>%
        mutate(PUF_30_DAY_MORT_CD_F = fct_recode(PUF_30_DAY_MORT_CD,
                                                 "Alive_30" = "0",
                                                 "Dead_30" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(PUF_90_DAY_MORT_CD_F = fct_recode(PUF_90_DAY_MORT_CD,
                                                 "Alive_90" = "0",
                                                 "Dead_90" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(LYMPH_VASCULAR_INVASION_F = fct_recode(LYMPH_VASCULAR_INVASION,
                                                      "Neg_LymphVasc_Inv" = "0",
                                                      "Pos_LumphVasc_Inv" = "1",
                                                      "N_A" = "8",
                                                      "Unknown" = "9")) %>%
        mutate(RX_HOSP_SURG_APPR_2010_F = fct_recode(RX_HOSP_SURG_APPR_2010,
                                                     "No_Surg" = "0",
                                                     "Robot_Assist" = "1",
                                                     "Robot_to_Open" = "2",
                                                     "Endo_Lap" = "3",
                                                     "Endo_Lap_to_Open" = "4",
                                                     "Open_Unknown" = "5",
                                                     "Unknown" = "9")) %>%
        mutate(All = "All") %>%
        mutate(All = factor(All)) %>%
        mutate(REASON_FOR_NO_SURGERY_F = fct_recode(REASON_FOR_NO_SURGERY,
                                                    "Surg performed" = "0",
                                                    "Surg not recommended" = "1",
                                                    "No surg due to pt factors" = "2",
                                                    "No surg, pt died" = "5",
                                                    "Surg rec, not done" = "6",
                                                    "Surg rec, pt refused" = "7",
                                                    "Surg rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(SURGERY_YN = ifelse(REASON_FOR_NO_SURGERY == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_SURGERY == "9",
                                          "Ukn",
                                          "No"))) %>%
        mutate(SURG_TF = case_when(SURGERY_YN == "Yes" ~ TRUE,
                             SURGERY_YN == "No" ~ FALSE,
                             TRUE ~ NA))  %>%
        mutate(REASON_FOR_NO_RADIATION_F = fct_recode(REASON_FOR_NO_RADIATION,
                                                    "Rad performed" = "0",
                                                    "Rad not recommended" = "1",
                                                    "No Rad due to pt factors" = "2",
                                                    "No Rad, pt died" = "5",
                                                    "Rad rec, not done" = "6",
                                                    "Rad rec, pt refused" = "7",
                                                    "Rad rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(RADIATION_YN = ifelse(REASON_FOR_NO_RADIATION == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_RADIATION == "9",
                                          NA,
                                          "No"))) %>%
        mutate(SURGRAD_SEQ_F = fct_recode(RX_SUMM_SURGRAD_SEQ,
                                                   "None or Surg or Rad" = "0",
                                                   "Rad before Surg" = "2",
                                                   "Surg before Rad" = "3",
                                                   "Rad before and after Surg" = "4",
                                                   "Intraop Rad" = "5",
                                                   "Intraop Rad plus other" = "6",
                                                   "Unknown" = "9")) %>%
        mutate(SURG_RAD_SEQ = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                     "Surg Alone",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                            "Rad Alone",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0",
                                                   "No Treatment",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2",
                                                          "Rad then Surg",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3",
                                                                 "Surg then Rad",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4",
                                                                        "Rad before and after Surg",
                                                                        "Other"))))))) %>%
        mutate(SURG_RAD_SEQ = fct_relevel(SURG_RAD_SEQ,
                                          "Surg Alone",
                                          "Surg then Rad",
                                          "Rad Alone")) %>%
        mutate(CHEMO_YN = fct_collapse(RX_SUMM_CHEMO,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01", "02", "03"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(SURG_RAD_SEQ_C = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                     "Surg, No rad, No Chemo",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                            "Rad, No Surg, No Chemo",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                                   "No Surg, No Rad, No Chemo",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "No",
                                                          "Rad then Surg, No Chemo",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "No",
                                                                 "Surg then Rad, No Chemo",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "No",
                                                                        "Rad before and after Surg, No Chemo",
                                ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                       "Surg, No rad, Yes Chemo",
                                       ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                              "Rad, No Surg, Yes Chemo",
                                              ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                                     "No Surg, No Rad, Yes Chemo",
                                                     ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "Yes",
                                                            "Rad then Surg, Yes Chemo",
                                                            ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "Yes",
                                                                   "Surg then Rad, Yes Chemo",
                                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "Yes",
                                                                          "Rad before and after Surg, Yes Chemo",
                                                                          "Other"))))))))))))) %>%
        mutate(SURG_RAD_SEQ_C = fct_infreq(SURG_RAD_SEQ_C)) %>%
        mutate(T_SIZE = as.numeric(TUMOR_SIZE)) %>%
        mutate(T_SIZE = ifelse(T_SIZE == 0,
                                "No Tumor",
                                ifelse(T_SIZE > 0 & T_SIZE < 10 | T_SIZE == 991,
                                       "< 1 cm",
                                       ifelse(T_SIZE >= 10 & T_SIZE < 20 | T_SIZE == 992,
                                              "1-2 cm",
                                              ifelse(T_SIZE >= 20 & T_SIZE < 30 | T_SIZE == 993,
                                                     "2-3 cm",
                                                     ifelse(T_SIZE >= 30 & T_SIZE < 40 | T_SIZE == 994,
                                                            "3-4 cm",
                                                            ifelse(T_SIZE >= 40 & T_SIZE < 50 | T_SIZE == 995,
                                                                   "4-5 cm",
                                                                   ifelse(T_SIZE >= 50 & T_SIZE < 60 | T_SIZE == 996,
                                                                          "5-6 cm",
                                                                          ifelse(T_SIZE >= 60 & T_SIZE <= 987 |
                                                                                         T_SIZE == 980 | T_SIZE == 989 |
                                                                                         T_SIZE == 997,
                                                                          ">6 cm",
                                                                          ifelse(T_SIZE == 988 | T_SIZE == 999,
                                                                                 "NA_unk",
                                                                                 "Microscopic focus")))))))))) %>%
        mutate(T_SIZE = factor(T_SIZE)) %>%
        mutate(T_SIZE = fct_relevel(T_SIZE,
                                     "No Tumor", "Microscopic focus", "< 1 cm", "1-2 cm", "2-3 cm", "3-4 cm",
                                       "4-5 cm", "5-6 cm", ">6 cm", "NA_unk")) %>%
        mutate(mets_at_dx = case_when(CS_METS_DX_LUNG == "1" ~ "Lung",
                                      CS_METS_DX_BONE == "1" ~ "Bone",
                                      CS_METS_DX_BRAIN == "1" ~ "Brain",
                                      CS_METS_DX_LIVER == "1" ~ "Liver",
                                      TRUE ~ "None/Other/Unk/NA")) %>%
        mutate(MEDICAID_EXPN_CODE = fct_recode(MEDICAID_EXPN_CODE,
                                               "Non-Expansion State" = "0",
                                               "Jan 2014 Expansion States" = "1",
                                               "Early Expansion States (2010-13)" = "2",
                                               "Late Expansion States (> Jan 2014)" = "3",
                                               "Suppressed for Ages 0 - 39" = "9"))  %>%
        mutate(EXPN_GROUP =  case_when(MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Post-Expansion",
                                       
                                       MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% 
                                          c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013") ~ "Pre-Expansion",
               
                                       MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2010", "2011", "2012", "2013", "2014", "2015") ~ "Post-Expansion",
                                       
                                        MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", "2009") ~ "Pre-Expansion",

                                       MEDICAID_EXPN_CODE %in% c("Non-Expansion State") ~ "Pre-Expansion",

                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") ~ "Pre-Expansion",
                    
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") & 
                                        YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Exclude",
                                       
                                       MEDICAID_EXPN_CODE == "Suppressed for Ages 0 - 39" ~ "Exclude")) %>%
  
  mutate(pre_2014 = YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013")) %>%
  
  mutate(mets_at_dx_F = ifelse(mets_at_dx == "None/Other/Unk/NA", FALSE, TRUE)) %>% 
  
  mutate(Tx_YN = ifelse(SURG_RAD_SEQ == "No Treatment" & CHEMO_YN == "No", FALSE, 
                        ifelse(CHEMO_YN == "Ukn", NA, 
                               TRUE)))

fact_vars_2 <- c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "AGE_F", "SEX_F", "RACE_F",
                 "HISPANIC", "INSURANCE_F", "INCOME_F", "EDUCATION_F", "U_R_F",
                 "CDCC_TOTAL_BEST", "CLASS_OF_CASE_F", "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "HISTOLOGY",
                 "BEHAVIOR", "GRADE_F", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M", "TNM_PATH_STAGE_GROUP",
                 "MARGINS", "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "mets_at_dx")


dat <- dat %>%
        mutate_at(fact_vars_2, funs(factor(.)))

```

# functions
```{r}
p_table <- function(tab_data, ...) {
  tab_data_2 <- deparse(substitute(tab_data))
  
  table_p <- do.call(CreateTableOne, 
                     list(data = as.name(tab_data_2), includeNA = TRUE, ...))
  table_p_out <- print(table_p,
                       showAllLevels = TRUE,
                       printToggle = FALSE)
  kable(table_p_out,
        align = "c")
}
```




```{r}
uni_var <- function(test_var, data_imp) {

                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")

        
        f <- as.formula(paste("Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)",
                              as.name(test_var),
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))

        km_fit <- do.call("survfit", list(formula = f, data = as.name(data_imp_2)))

        print(km_fit)
        cat("\n")

        print(summary(km_fit, times = c(12, 24, 36, 48, 60, 120)))
        cat("\n")


        cat("\n")
        cat("\n")
        cat("   \n## Univariable Cox Proportional Hazard Model for: ", test_var)
        cat("\n")
        cat("\n")


        n_levels <- nlevels(data_imp[[test_var]])

        if(n_levels == 1){
                print("Only one level, no Cox model performed")
                cat("\n")

        } else {


                cox_fit <- do.call("coxph", list(formula = f, data = as.name(data_imp_2)))

                print(summary(cox_fit))
                cat("\n")
                
                do.call("ggforest",
                         list(model = cox_fit, data = as.name(data_imp_2)))


        }

        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Unadjusted Kaplan Meier Overall Survival Curve for: ", test_var)


        p <- do.call("ggsurvplot",
                     list(fit = km_fit, data = as.name(data_imp_2),
                          palette = "jco", censor = FALSE, legend = "right",
                          linetype = "strata", xlab = "Time (Months)"))

        print(p)

}

```


# Extract Data of Interest

```{r}
# Melanoma
site_code <- c("C440", "C441", "C442", "C443", "C444", "C445", "C446", "C447", "C448", "C449", "C510", "C511", "C512", "C518", "C519", "C600", "C601", "C602", "C608", "C609", "C632")
histo_code <- c("8720", "8741", "8746", "8721", "8722", "8723", "8730", "8740", "8742", "8743", "8744", "8745", "8761")

behavior_code <- c("3")

data <- dat %>%
        filter(BEHAVIOR %in% behavior_code) %>%
        filter(PRIMARY_SITE %in% site_code) %>%
        filter(HISTOLOGY %in% histo_code) %>%
        filter(is.na(PUF_VITAL_STATUS) == FALSE) %>%
        filter(is.na(DX_LASTCONTACT_DEATH_MONTHS) == FALSE) %>%
        filter(INSURANCE_STATUS %in% c("0", "1", "2", "3", "4"))
   # filter(AGE >= 18) %>%
   #      filter(TNM_CLIN_M %in% c("c0")) %>%
   #      filter(SEQUENCE_NUMBER == "00") %>%
   #      filter(CLASS_OF_CASE %in% c("10", "12", "14", "22")) %>%
        

no_Excludes <- as.data.frame(data %>% 
                               filter(EXPN_GROUP != "Exclude") 
                             %>% droplevels())


file_path <- c("/Users/beastatlife/Google Drive/Penn/Research/Barbieri/NCDB")
save(data,
      file = paste0(file_path, "/melanoma_data.Rda"))
```

```{r loadData}
#load("melanoma_data.Rda")
```




Data including skin tumors was obtained from the NCBD on October 7, 2019. Cases that were included in this analysis were those with:

1. Site codes: `r site_code`
2. Histology codes: `r histo_code`
3. Behavior codes: `r behavior_code`


Patients were excluded if they didn't have values for either follow up or vital status.

Patients were excluded if they had surgery to a distant site using `RX_SUMM_SURG_OTH_REGDIS`. This was done to avoid confounding of different surgical procedures. We are only interested in surgery at the primary site. These distant site surgeries were being counted in the surgery/radiation sequence and thus to simplify analysis they were removed. 

```{r}

data %>%
        CreateTableOne(data = .,
                     vars = c("RX_SUMM_SURG_OTH_REGDIS"),
                     includeNA = TRUE) %>%
        print(.,
              showAllLevels = TRUE)

data <- data %>%
        filter(RX_SUMM_SURG_OTH_REGDIS == "0") 
```


Race was grouped as white, black, asian, other/unknown
Stage was grouped into 0, I, II, III, IV, NA_Unknown, stage 0 was removed
Whether surgery was performed was based on the `REASON_FOR_NO_SURGERY` variable. The `SURGERY_YN` variable was classified as 'Yes', 'No', or 'Unknown'.


Whether radiation was performed was based on the `REASON_FOR_NO_RADIATION` variable. The `RADIATION_YN` variable was classified as 'Yes', 'No', or 'Unknown'.



##Table of variables for all cases:

```{r}

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "PRIMARY_SITE", "HISTOLOGY_F_LIM", "HISTOLOGY_F", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE", "EXPN_GROUP", "SITE_TEXT"))



p_table(no_Excludes,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "PRIMARY_SITE", "HISTOLOGY_F_LIM", "HISTOLOGY_F", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE","SITE_TEXT"), 
        strata = "EXPN_GROUP")


p_table(data,
        vars = c("YEAR_OF_DIAGNOSIS"),
        strata = c("MEDICAID_EXPN_CODE"))
```


```{r}

preExpMedicare  <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion" & INSURANCE_F == "Medicare"))
postExpMedicare <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion" & INSURANCE_F == "Medicare"))

# p = 0.25 when comparing change in proportion of patients with Medicare before and after ACA expansion
prop.test(c(preExpMedicare, postExpMedicare), 
          c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))

preExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion") %>% 
                            filter(INSURANCE_F == "None"))
postExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion") %>% 
                             filter(INSURANCE_F == "None"))

# Significant decrease in the overall proportion of patients without insurance after ACA expansion 
prop.test(c(preExpNoInsurance, postExpNoInsurance), 
          c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))

p_table(no_Excludes, strata = "EXPN_GROUP", vars = "DX_RX_STARTED_DAYS")

data <- data %>% mutate(Insured = INSURANCE_F != "Unknown")

```




#Kaplan Meier Analysis


##All

```{r}
uni_var(test_var = "All", data_imp = data)
```

##Facility Type
```{r}
uni_var(test_var = "FACILITY_TYPE_F", data_imp = data)
```

##Facility Location

```{r}
uni_var(test_var = "FACILITY_LOCATION_F", data_imp = data)
```

##Facility Geography

```{r}
uni_var(test_var = "FACILITY_GEOGRAPHY", data_imp = data)
```

##Age Group

```{r}
uni_var(test_var = "AGE_F", data_imp = data)
```

##Age Group
```{r}
uni_var(test_var = "AGE_40", data_imp = data)
```

##Gender

```{r}
uni_var(test_var = "SEX_F", data_imp = data)
```

##RACE_F

```{r}
uni_var(test_var = "RACE_F", data_imp = data)
```

##Hispanic

```{r}
uni_var(test_var = "HISPANIC", data_imp = data)
```

##Insurance Status

```{r}
uni_var(test_var = "INSURANCE_F", data_imp = data)
```

##Overall Survival pre/post-ACA expansion

```{r}
uni_var(test_var = "EXPN_GROUP", data_imp = no_Excludes)
```


<!-- ##Income -->

<!-- ```{r} -->
<!-- class(data$INCOME_F) -->
<!-- uni_var(test_var = "INCOME_F", data_imp = data) -->
<!-- ``` -->

##Education

```{r}
uni_var(test_var = "EDUCATION_F", data_imp = data)
```

##Urban/Rural

```{r}
uni_var(test_var = "U_R_F", data_imp = data)
```

##Class (treatment at performing facility)

```{r}
uni_var(test_var = "CLASS_OF_CASE_F", data_imp = data)
```

##Year

```{r}
uni_var(test_var = "YEAR_OF_DIAGNOSIS", data_imp = data)
```

##Primary Site

```{r}
uni_var(test_var = "SITE_TEXT", data_imp = data)
```


##Histology

```{r}
uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)
```

<!-- ##Behavior -->

<!-- ```{r} -->
<!-- uni_var(test_var = "BEHAVIOR", data_imp = data) -->
<!-- ``` -->

##Grade

```{r}
uni_var(test_var = "GRADE_F", data_imp = data)
```

##Clinical T Stage

```{r}
uni_var(test_var = "TNM_CLIN_T", data_imp = data)
```

##Clinical N Stage

```{r}
uni_var(test_var = "TNM_CLIN_N", data_imp = data)
```

<!-- ##Clinical M Stage -->

<!-- ```{r} -->
<!-- uni_var(test_var = "TNM_CLIN_M", data_imp = data) -->
<!-- ``` -->

##Clinical Stage Group

```{r}
uni_var(test_var = "TNM_CLIN_STAGE_GROUP", data_imp = data)
```

##Pathologic T Stage

```{r}
uni_var(test_var = "TNM_PATH_T", data_imp = data)
```

##Pathologic N Stage

```{r}
uni_var(test_var = "TNM_PATH_N", data_imp = data)
```

##Pathologic M Stage

```{r}
uni_var(test_var = "TNM_PATH_M", data_imp = data)
```

##Pathologic Stage Group

```{r}
uni_var(test_var = "TNM_PATH_STAGE_GROUP", data_imp = data)
```

##Margins
```{r}
uni_var(test_var = "MARGINS", data_imp = data)
```

##Margins Yes/No
```{r}
#uni_var(test_var = "MARGINS_YN", data_imp = data)
```

##30 Day Readmission

```{r}
uni_var(test_var = "READM_HOSP_30_DAYS_F", data_imp = data)
```

##Radiation Type

```{r}
uni_var(test_var = "RX_SUMM_RADIATION_F", data_imp = data)
```


##Lymphovascular Invasion

```{r}
uni_var(test_var = "LYMPH_VASCULAR_INVASION_F", data_imp = data)
```

##Endoscopic/Robotic

```{r}
uni_var(test_var = "RX_HOSP_SURG_APPR_2010_F", data_imp = data)
```

##Surgery Radiation Sequence 

```{r}
uni_var(test_var = "SURG_RAD_SEQ", data_imp = data)
```

##Surgery Yes/No

```{r}
uni_var(test_var = "SURGERY_YN", data_imp = data)
```

##Radiation Yes/No

```{r}
uni_var(test_var = "RADIATION_YN", data_imp = data)
```

##Chemo Yes/No

```{r}
uni_var(test_var = "CHEMO_YN", data_imp = data)
```


##Treatment Yes/No
```{r}
uni_var(test_var = "Tx_YN", data_imp = data)
```

##Metastases at Dx
```{r}
uni_var(test_var = "mets_at_dx_F", data_imp = data)
```

<!-- ##Tumor Size -->

<!-- ```{r} -->
<!-- uni_var(test_var = "T_SIZE", data_imp = data) -->
<!-- ``` -->

#Tumor specific Variables


###Node Size


#Cox Proportional Hazard Ratio

##Model #1

###Full analysis

```{r}
model_one <- coxph(Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)
                     ~ SURG_RAD_SEQ + INSURANCE_F + AGE + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F,
                     data = data)
model_one %>% summary()


```

###Summary of Model

```{r}
model_one %>%
        tidy(., exponentiate = TRUE) %>%
        select(term, estimate, conf.low, conf.high, p.value) %>%
        rename(Variable = term,
               Hazard_Ratio = estimate) %>%
        tbl_df %>%
        print(n = nrow(.))

```

# Predictors of Surgery
```{r}

fit_surg <- glm(SURG_TF ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP,
   data = data %>% filter(SURGERY_YN != "Ukn") %>% droplevels() %>% mutate(SURGERY_YN = as.logical(SURGERY_YN)))

summary(fit_surg)

exp(cbind("Odds ratio" = coef(fit_surg), confint.default(fit_surg, level = 0.95)))
```

# Predictors of Metastasis at Time of Diagnosis, limit to those cases where data
#  about expansion status is available (> Age 39, non-ambiguous status states)
```{r}

fit_mets <- glm(mets_at_dx_F ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP,
   data = no_Excludes)

summary(fit_mets)

exp(cbind("Odds ratio" = coef(fit_mets), confint.default(fit_surg, level = 0.95)))
```